Apparatus, method and system for designing and trading macroeconomic investment views

ABSTRACT

The disclosure details the implementation of an apparatus, method, and system for a macroeconomic equity investment design and trade system (the Wavefront system). The disclosure teaches a set of quantitative tools to help investors design trades around macro themes. Part of the approach is a linked set of models called Wavefronts, which describe how economic shocks ripple through the economy into company performance, market value and equity returns in the US market. In one embodiment, the modeling may be viewed as having in three parts. The first converts an economic shock into a comprehensive set of shifts in the economy. The second takes those economic shifts and drives them into company fundamentals. The third values those fundamentals based on what the market normally pays. As a consequence, the Wavefront system maps economic views and risks into predictions of what the market will pay for those changes, and the industries and companies that will over-and under-perform, which allows for and results in the construction of more risk-efficient portfolios. In an alternative embodiment, the Wavefront system may also inverse the progression of the three parts to uncover and move industry specific information to uncover macroeconomic themes.

RELATED APPLICATION

Applicant hereby claims priority under 35 USC §119 for U.S. provisionalpatent application Ser. No. 60/562,818 filed Apr. 16, 2004, entitled“Apparatus, method and system for designing macroeconomic equityinvestments,” and under 35 U.S.C. §§365 and 371 for InternationalApplication No. PCT/US2005/012991 filed Apr. 15, 2005, entitled“Apparatus, Method and System for Designing and Trading MacroeconomicInvestment Views.” The entire content of the aforementioned applicationsare expressly incorporated herein by reference.

FIELD

The present invention is directed generally to an apparatus, method, andsystem of building portfolios, and more particularly, to an apparatus,method and system to design and trade macroeconomic investment views.

BACKGROUND

Computerized marketplaces of all kinds range from simple classified adbulletin boards to complex mainframe-based market systems such asNASDAQ, which offers a real-time market-making system for tens ofthousands of securities brokers. All modern stock, bond and commodityexchanges are supported by underlying computerized databases and relatedsystems, which enable them to function.

Trading systems for items having substantial value generally are anautomated version of a manual trading process. For example, securitiestrading systems are based on a model wherein a customer contacts aso-called retail broker to place an order. The broker, in turn, submitsthe order to a dealer who executes the order and returns an orderconfirmation to the broker. Other known systems automate the open outcryprocess used in trading pits. Importantly, securities trading is heavilyregulated. Many of the terms and conditions prevalent in securitiestrades are limited by convention and regulation. Automated securitiestrading systems necessarily reflect these constraints. Such financialsystems typically rely on underlying information technology systems,user interface, networks, and/or other core technologies.

SUMMARY

The present invention provides for the implementation of an apparatus,method, and system for macroeconomic equity investment design and tradesystem (the Wavefront system). The Wavefront system improves upon equitydesign and trade tools by providing a set of quantitative tools to helpinvestors design trades around macro themes. Part of the approach is alinked set of models called Wavefronts, which describe how economicshocks ripple through the economy into company performance, market valueand equity returns in the US market. In one embodiment, the modeling maybe viewed as having three parts. The first converts an economic shockinto a comprehensive set of shifts in the economy, which affect certainrelated macroeconomic variables. The second takes those economic shiftsand drives them into company fundamentals by using various economicdrivers. The third values those fundamentals based on what the marketnormally pays. As a consequence, the Wavefront system enables mapping ofeconomic views and risks into predictions of what the market will payfor changes in those views and risks; the Wavefront system also enablesthe identification of industries and companies that will over andunder-perform based on its mappings. The Wavefront system allows for andresults in the construction of more risk-efficient portfolios. Inanother embodiment, by reversing the modeling process, the Wavefrontsystem may show how the market has been trading various macro themes andallow for more accurate reads on the market. This provides asophisticated view of how market focus is shifting, how particularevents are being interpreted and whether a particular theme has alreadyplayed out. Such an embodiment allows the Wavefront system users to gaina better understanding of how the equity markets are likely to processnew information.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various non-limiting, example,inventive aspects in accordance with the present disclosure:

FIG. 1 is of an information and logic flow diagram illustratingembodiments of the present invention of a disclosed Macroeconomic EquityInvestment Design and Trade System;

FIG. 2 is of an information topology and flow diagram illustratingembodiments of the present invention of a Wavefront;

FIG. 3 is of a chart diagram illustrating embodiments of the presentinvention to show the valuation impact of an economic event;

FIG. 4 is of a data-flow diagram illustrating embodiments of the presentinvention to link developments in an economy to performance and value ofsectors, industries, and/or companies;

FIG. 5 is of a logic-flow diagram illustrating embodiments of thepresent invention of economic Wavefront development;

FIG. 6 is of a table diagram illustrating embodiments of the presentinvention of an pattern of macroeconomic variables affected by aneconomic event;

FIG. 7 is of an information topology and flow diagram illustratingembodiments of the present invention of an industry Wavefront;

FIG. 8 is of a logic-flow diagram illustrating embodiments of thepresent invention of industry Wavefront development;

FIG. 9 is of a chart diagram illustrating embodiments of the presentinvention where industry Wavefronts differ for similar economic events;

FIG. 10 is of an information topology and flow diagram illustratingembodiments of the present invention of a fundamental Wavefront;

FIGS. 11-13 are of a logic-flow diagrams illustrating embodiments of thepresent invention of fundamental Wavefront development;

FIGS. 14-20 are of an information topology and flow diagramsillustrating embodiments of the present invention of a valuationWavefront;

FIG. 21 is of an logic-flow diagram illustrating embodiments of thepresent invention to build a trade;

FIGS. 22-26 are chart diagrams illustrating embodiments of the presentinvention exemplifying the building of a trade;

FIG. 26 is of a table illustrating embodiments of the present inventionsummarizing analysis of a menu of macro trades.

FIGS. 27-28 are chart diagrams illustrating embodiments of the presentinvention exemplifying the reading of the market;

FIGS. 28 and 29 are examples of the consumer discretionary ETF (XLY)2715 of FIG. 27;

FIG. 30 shows one example of a Wavefront Growth Basket. Wavefrontbaskets exploit the benefits of “slicing and dicing;”

FIG. 31 shows one version of an economic scenario, in which GDP growthrises 0.5% but interest rates remain stable;

FIG. 32 revisits the menu of alternative trade implementations designedto exploit a growth view;

FIG. 33 shows a stronger consumer spending outlook that takes place inan environment where growth is unchanged;

FIG. 34 highlights the risk/reward metrics relative to the pure consumercase;

FIG. 35 shows the major ETF pairs have sizable negative exposure to GDPgrowth;

FIG. 36 shows the expected returns of FIG. 33;

FIG. 37 sets out a macro scenario in which oil prices rise, drivinggrowth lower;

FIG. 38 shows three ways to trade the macro scenario in FIG. 37;

FIG. 39 looks across the risk/reward metrics;

FIG. 40 shows a scenario that represents a “pure” rates scenario;

FIG. 41 compares the various implementations with significant variationacross different implementations;

FIG. 42 shows a Wavefront Foreign Growth Basket designed to allowinvestors to exploit the economic scenario portrayed therein;

FIG. 43 compares foreign growth using trade performance metrics;

FIG. 44 shows a mechanism to identify good trades;

FIG. 45 is of a block diagram illustrating embodiments of the presentinvention of a Macroeconomic Equity Investment Design and Trade Systemcontroller.

APPENDIX 1 is of a table illustrating embodiments of the presentinvention of an pattern of macroeconomic variables affected by aneconomic event;

APPENDICES 2A-C are of a table illustrating embodiments of the presentinvention of a modified global industry classification standard;

APPENDICES 3A-B is of a table illustrating embodiments of the presentinvention of sales and valuation impacts on the basis of GDP and PCEeconomic drivers;

APPENDIX 4 is of an expanded set of risk metrics for various tradeimplementations;

APPENDIX 5 provides background on the Wavefront Market Monitor;

APPENDIX 6 is of a set of consumer variables;

APPENDIX 7 provides background Wavefront China Growth Basket.

The leading number of each reference number within the drawingsindicates the first figure in which that reference number is introduced.As such, reference number 101 is first introduced in FIG. 1. Referencenumber 201 is first introduced in FIG. 2, etc.

DETAILED DESCRIPTION

The Wavefront system improves upon equity design and trade tools. Partof the approach is a linked set of models called Wavefronts, whichdescribe how economic shocks ripple through the economy into companyperformance, market value and equity returns in the US market. In oneembodiment, the modeling may be viewed as having three parts. The firstconverts an economic shock into a comprehensive set of shifts in theeconomy. The second takes those economic shifts and drives them intocompany fundamentals. The third values those fundamentals based on whatthe market normally pays. As a consequence, the Wavefront system enablesmapping of economic views and risks into predictions of what the marketwill pay for changes in those views and risks; the Wavefront system alsoenables the identification of industries and companies that will overand under-perform based on its mappings. The Wavefront system allows forand results in the construction of more risk-efficient portfolios.

Wavefronts

FIG. 1 is of an information and logic flow diagram illustratingembodiments of the present invention of a disclosed Macroeconomic EquityInvestment Design and Trade System. This overview of the system showsthat the Wavefront system provides four interactive components thatbuild upon themselves and allow for iteration. In general, the Wavefrontsystem comprises the development of a Wavefront model 105, from which afinancial trade may be built 120, risk (e.g., of a portfolio) may bebetter managed 130, and any number of markets may be more accuratelyread 140. In one embodiment, upon obtaining a better read on the market,such results may be used iteratively to further develop new Wavefronts105.

In one embodiment, a Wavefront may be described as an economic modelmapping the effects of macro-economic events into micro-economic firmvaluations. In one embodiment, such a Wavefront model is determined by:(1) ascertaining an economic event 107, (2) developing an economicWavefront from the economic event 109, (3) developing an industryWavefront from the economic Wavefront 111, (4) developing a fundamentalWavefront from the industry Wavefront 113, and developing a valuationWavefront from the fundamental Wavefront 115. Although in manyinstances, investors will choose to determine company valuations on thebasis of some macro-economic event 107-115, the inverse operation may beperformed and a Wavefront may be used to map back from corporatevaluations to macro-economic events 115-107. In this inverse embodiment,certain fundamental indicators that affect valuation may be used touncover otherwise unnoticed economic events.

FIG. 2 is of an information topology and flow diagram illustratingembodiments of the present invention of a Wavefront model. In thisembodiment, the Wavefront system is illustrated in greater detail in aspecial arrangement where the Wavefront is modeled in segments. In oneembodiment, five cascading model segments comprise the Wavefront. Thesegments include: (1) an economic event 207, (2) an economic Wavefront109, (3) an industry Wavefront 111, (4) a (company) fundamentalWavefront 113, and (5) a valuation Wavefront 115 (i.e., the entirety ofthese five segments resulting in the valuation Wavefront, which maybereferred to, simply, in toto as “Wavefront”).

For ease of cognition, the Wavefront is likened to a water ripplingthrough a pond. The pond itself represents the economy. An economicevent is likened to a rock hitting the water of a pond. Just as a rockdisturbs and shocks the water of a pond, so does an economic eventcreate Wavefronts that ripples and spreads through an economy. As such,Wavefronts summarize the (economic ripple) pattern 203 of how aparticular economic event 207 spreads 205 across trade and industrysegments 109, 111, 113, 115 as an economic Wavefront 109 through toindustry 111, out into company fundamentals 113, which affectperceptions of market value 115, and which in turn flow into and affectstock and/or investment returns 240 in various company and industrysectors 250. In short, an economic shock that creates ripples throughthe entire economy may be described as an Wavefront, and in turn, theWavefront can describe the effects of its wake as between any economicevents 207 (points A 207) and company and industry sectors (points B250).

Each crest 203 in the Wavefront acts as a cause to affect trade andindustry segments 109, 111, 113, 115, 250. As such, the Wavefront andtrade and industry segments are inter-linked. For example, a valuationWavefront 115 might show the pattern 240 of expected returns acrossindustries in response to a 1% upward revision to expectations of GDPgrowth 245, which acts as the source of the economic event 207.

FIG. 3 is of a chart diagram illustrating embodiments of the presentinvention to show the valuation impact of an economic event. Continuingthe example of FIG. 2 of a 1% upward revision to expectations of GDP245, FIG. 3 expands the detail and thus illustrate the impact of thesurprising economic event 207, 245 on the valuation of variouscompanies, industries, sectors, and/or the like 250, 305. In thisexample the Wavefront system shows that the economic event caused avaluation Wavefront 115 of FIG. 2 to change the valuation in Machinery,Diversified Metals & Mining, Airlines, Steel, Communications Equipment,Chemical, Construction Materials and Transportation sectors positively.Similarly, the Wavefront system determined that the economic eventcaused a valuation Wavefront to affect the valuation in Integrated Oil &Gas; Regional Banks; Hotels, Restaurants & Leisure; Beverages; FoodProducts & Soft Drinks; and Household Durables negatively.

It should be noted that just as different waves with different originsmay overlap in a pond, the interaction between different economic shocksand their Wavefront consequences build on each other. As more shocksoccur, an equity market's movements become a pattern of Wavefrontcross-currents. As will be discussed below in greater detail, theWavefront system is designed to help analyze and navigate thesecomplicated Wavefront cross-currents. As such, Wavefronts (like ripplesin a pond) are scalable and additive. Complicated economic scenarios(for instance combining currency, interest rate, and oil views) can betreated as the weighted sum of the economic Wavefronts stemming from ashock 207 that affects various economic variables 255 such as currency,interest rates, and oil prices. Even structural change within theeconomy can be treated by combining the appropriate Wavefronts.

Economic Events

As was illustrated in FIG. 2, an economic event may be any one or moreof a broad array of macroeconomic variables 245, 255. Examples ofmacroeconomics variables include: 10 year notes, capacity utilization,consumer, corporate rates, Consumer Price Index (CPI), consumerspending, currencies (e.g., the dollar), employment, exchange rates,federal funds, foreign gross domestic product, Gross Domestic Product(GDP), growth rates (i.e., of any of these variables), industrialproduction, interest rates, investment, oil prices, productivity, priceto cash earnings (PCE), wages, etc. The economic event is where a givenone or more of the macroeconomic variables departs from its expectedlevel or value at and/or for a given time. For this reason, an economicevent is said to be surprising or a shock relative to what was expected(i.e., an economic shock or economic surprise). In an alternativeembodiment, the departure in macroeconomic variables from expectedlevels is not actual, but theorized by an investor, portfolio managerand/or the like. In yet another embodiment, the departing macroeconomicvariable is determined by inversely following a Wavefront as discussedin FIG. 1, working back from corporate and/or industry indicators, e.g.,annual earnings reports for a given corporation and/or sector.Regardless of how it is chosen, once a departing economic variable (orset of variables) is selected, the investor is said to have formed a“view” where at some point in time there will be a departure fromexpected values by some chosen subset of macroeconomic variables. Ofcourse if the investor selects an actual macroeconomic variable that hassurprisingly departed from estimates, the point in time of the departureis known. As will be discussed in greater detail below, such departuresin a subset of variables will affect other macroeconomic variables.

Linking Macro Events to Equities

Macroeconomic views are fundamental drivers of relative performancewithin equity markets. Linking macroeconomic views to equity marketoutcomes is a notoriously difficult task. Economic events are complex.Changes in one part of the economy affect many other parts and affectcompany performance and valuations through a range of channels. As such,portfolio managers may develop a view that the economy is different fromwhat they believe the market expects. Any of these differing views mayencompass, involve, predict, be a source of, result in, and/or otherwisebe a surprising economic event 245. Examples of the types of differingviews a portfolio manger might development include views where: growthexpectations are likely to sour; oil prices are going to fall; themarket is overestimating domestic growth but underestimating foreigngrowth; or some combination of all of these. Such differing views mayraise numerous questions that the portfolio manager will need to answer,however, there have been no effective tools to help them assess theeffects of these differing views on the market. Portfolio managers maybenefit from tools to let them make assessments regarding their variousdiffering views, such as: How should they position a portfolio toexploit this view? What other risks are created by positioning that way?What are the risks to the other trades in the portfolio? And has themarket already been trading this theme?

Linking Tool

FIG. 4 is of a data-flow diagram illustrating embodiments of the presentinvention to link developments in an economy to performance and value ofsectors, industries, and/or companies. Although traditionally makingconnections between economic views and market outcomes has vexedinvestors, the Wavefront system provides tools allowing investors tomake these links. In one embodiment, the link from an economic eventand/or view and outcome can be shown using three sets of interlockingmodels 405, 410, 420 that link developments in the US economy to theperformance and value of more than 2,200 currently active companiesacross 59 industries, based on the experience, which are stored in theWavefront system database as will be described in greater detail in FIG.45.

The first—a model of the US macro economy—converts a view on aparticular economic shock (an oil price spike, for instance) into acomprehensive set of changes in all the major economic variables thataffect company performance 405. This is the move between the economicevent 207 into the economic Wavefront 109 of FIG. 2.

The second—a set of industry and company models—takes those changes inthe economic outlook and drives them into the fundamentals of industryand company performance, particularly the key elements of Return onEquity (ROE) 410. This is the move between the economic Wavefront 109into the industry Wavefront 111 and through the fundamentals Wavefront113 of FIG. 2.

The third—a valuation model—values those shifts in company performance,setting out what the market is likely to pay for the changes to theoutlook. This valuation technique is called Market Based Valuation 415.This is the move from the fundamentals Wavefront 113 to the valuationWavefront 115 of FIG. 2.

As such, the Wavefront acts to unify these interlocking models 207, 109of FIG. 2 and 405; 111, 113 of FIG. 2 and 410; and 115 of Figure and415. This unification allows the Wavefront system to improve upontraditional systems. Some advantages include: multidimensionality—theWavefront system can distinguish between economic events that looksimilar—a rise in bond yields for instance—but have different underlyingcauses—growth improvement versus Fed tightening; flexibility—theWavefront system can isolate the impact of different shocks, allowinginvestors to think about scenarios that have never occurred or tocontrol for factors that they believe will be different; By modelingdown to the company level, the Wavefront system can aggregate baskets ofcompanies into many different kinds of indices or groups to analyze theeffect on their performance; comprehensive—the Wavefront system allowsinvestors to look not just at the impact of a scenario but at the risksand exposures to that view.

Wavefront Construct

While the above sections of the disclosure provided a general overviewof Wavefronts and some of their uses, what follows details theconstruction of Wavefronts. To that end, it should be noted thatWavefront models allow you to construct baskets having risk specificfactors and that Wavefronts provide cross-sectional insight rather thanjust time-series correlations. Details of the Wavefront segmentsdiscussed in FIGS. 1 and 2 follow.

Economic Wavefront

FIG. 5 is of a logic-flow diagram illustrating embodiments of thepresent invention of economic Wavefront development. As has already beendiscussed in FIGS. 2 and 3 above, an economic event is ascertained inthe development of the Wavefront 107 of FIG. 1. Thereafter, theWavefront system develops an economic Wavefront 109. In so doing, theWavefront system identifies the source of the economic Event itsdatabase after it is provided 107. The economic event may be provided asan entry in a spreadsheet field, in a database, and/or the like where acomputational engine may make use of various mathematical facilities andaccess, retrieve, and process the Wavefront system related data. In oneembodiment, the placement of a value in a designated field entry willdetermine the type of economic variable that is designated to be theeconomic shock. For example, any value entered in a spreadsheet with alabel of “GDP” will be deemed to be a GDP value. In an alternativeembodiment, values may be entered in a tokenized form that includes thevariable type and value; for example as an XML tokenized stream that maybe parsed by the Wavefront system. In yet another embodiment, specificfields are provided in a database table wherein entry and/or designationof the economic event may be entered. Upon obtaining the economic evententry 107, the Wavefront system may identify it by looking up that typeof economic variable in a macroeconomic variable table in the Wavefrontsystem database and also retrieve any associated and/or expected valuefor that type of economic variable 505. For more details regarding theWavefront system database and its tables, please see 4519 of FIG. 45.Thereby the Wavefront system can compare the provided macroeconomicevent with an expected value (retrieved from its database) for that typeof macroeconomic variable and determine a delta as between the twovalues

So continuing the above example where a user enters a GDP value, a deltais determined between a portfolio manager's current view of GDP providedto the Wavefront system and the retrieved and expected value in theWavefront system database. This delta in conjunction with the type ofmacroeconomic variable provided to the Wavefront system (i.e., in thiscase a difference between the portfolio managers view of GDP and anexpected value for GDP) may act as a key to search the Wavefront systemdatabase for a pattern of responses to this economic view by othermacroeconomic variables. In one embodiment, the Wavefront systemmaintains a table of patterns of affected macroeconomic variables 4519of FIG. 45.

Momentarily moving to FIG. 6, it is of a table diagram illustratingembodiments of the present invention of a pattern of macroeconomicvariables affected by an economic event. These tables list 605, 610observed correlations as between a delta from expected values 630 of aneconomic event 620 and the delta's effect on other macroeconomicvariables 625. So for example, a 1% increase in real GDP 630 is aneconomic event that affects a pattern of other economic variables 625where federal funds increase 0.9%, oil prices increase 0.1%, 10 yearnotes increase 0.5%, foreign exchange increases 2.3%, etc. 635. Thismultidimensional correlation of impacts by an economic event onto othermacroeconomic variables is generated through recording historical deltasand it results in a pattern of delta values for the macroeconomicvariables affected by any given economic event.

Another closer view of the tables 605, 610 highlights several notableaspects regarding the correlations and resulting affecting patterns.First, the database table tracks numerous observed deltas 620. There arenumerous levels of granularity that may be tracked regarding thesedeltas. In one embodiment, statistical analysis determines what deltasize results in appreciable effects on impacted macroeconomic variables625 and is the basis for determining the granularity of the deltas 620(e.g., if correlation analysis shows that 1% delta variances are thesmallest variances that cause appreciable deltas on impacted economicvariables 625, then a 1% delta would be a granular increment for thedeltas). In another embodiment, a set or fixed increment is used (e.g.,1% intervals of change from −100 (or less) to 0 to 100% or more).Another notable aspect is that the Wavefront system database tabletracks the impact on a given delta in multiple contexts—in that way thetable is multidimensional. For example, the impacts may vary for a givendelta based on the time of year. For example, in January a 1% increasein real GDP may result in a 1.1% increase in federal funds 655 whileresulting in only a 0.9% increase in federal funds in July 656. Numerousother factors may increase the number of dimensions (e.g., war/peacetime). Similarly, the granularity of time and other dimensions may alsobe varied. A third notable aspect is that some deltas have noappreciable impact on other economic variables. For example, a positive1% delta in federal funds in either January 670 or July 675 results inno appreciable affect on oil prices 677. In one embodiment, where thereis no appreciable affect by an economic event on a given economicvariable, then that economic variable is not part of the affectingpattern. In another embodiment, a 0% impact is part of the pattern andsaid to be an effect and is used in computations—this can make adifference when weighting the effects of multiple overlapping Wavefrontsin various linear algebra and other computations.

In one embodiment, 36 macroeconomic variables make up an affectedpattern for any given economic event 640. The pattern 640 shows anexcerpt of 19 such variable elements, where each of the elements areequally weighted, however, the weightings may and often do vary. In oneembodiment, these correlation tables are generated by tracking variancesin economic variables and statistically analyzing each variable forcorrelations based on its changes over time to the changes in othereconomic variables. Numerous modes of running averages may be used as abasis for building such a table. In one embodiment, the running averagesare computed over a specified interval. For example, statisticalcorrelations with economic events 620 are based on a running averageimpact on other economic variables 625 over a two to three year period.In such an example, the impact pattern values 635 for a given economicevent 620 represents an impact having duration of two to three years. Insuch a scenario, a 1% increase in GDP 630 would increase oil prices by0.1% for two to three years.

Moving back to FIG. 5, upon identifying the source of the economic event505 by looking up the appropriate type of macroeconomic variable in theWavefront system database, the Wavefront system will then identify apattern of macroeconomic variables affected by the economic event bylooking them up in the Wavefront system database 510 as has already beendiscussed, above, in FIG. 6. In so doing, the Wavefront system mayretrieve a pattern of responses from its database for the affectedmacroeconomic variables 515 as has already been discussed, above, inFIG. 6.

For each macroeconomic variable affected 520 by the economic event 107,the Wavefront system determines the duration of impact for the affectedmacroeconomic variable 525. In one embodiment, the Wavefront systemdetermines the duration of impact by querying its database andretrieving associated durations for the macroeconomic variable asalready detailed in FIG. 6. In one embodiment, the Wavefront systemmakes a query selection on the basis of an impact that spans specifiedinterval. For example, an economic impact may be specified as having adelta that will persist for 2 years. Furthering the example, if theWavefront system database has economic variable values with impactdurations of two years, the Wavefront system may query its database toaggregate affects on economic variables over a longer time, and sum theaggregate values to yield a total impact for the necessary and/orspecified duration 530. The Wavefront system will check if there aremore macroeconomic variables that are affected 535. If there are moreimpacted macroeconomic variables 535, the Wavefront system will iterate520 and continue with the next affected economic variable 545 that wasidentified 510, 515. Once all the impacted macroeconomic variables areexhausted and there are no more impacted variables 535, the Wavefrontsystem established that an economic surprise had an average incrementalimpact on a broad array of macro variables. In so doing, the Wavefrontsystem finished developing the economic Wavefront 109 because theWavefront system linked the resulting pattern of responses of othermacro variables that typically occur as a result of an economic surprise550 with the source of that economic surprise 107. In the aboveembodiments, that link was established utilizing a multi-equation modelof the US economy (for example, the Fed. Model), however, othereconomies may be employed in alternative embodiments.

In using the US economy as a basis, several advantages havematerialized. This model allows the Wavefront system to trace the impactof a surprise or shock to one economic variable into a set of changesacross the whole economy. The examples in FIG. 6 illustrate how thisworks. For example, a positive surprise to expectations of real GDP isassociated with higher interest rates, a flatter yield curve and astronger dollar 605. A surprise spike in oil prices is associated withlower GDP, lower interest rates and a steeper yield curve 605. Althoughonly the major variables are shown, in practice most variables in themodel respond to any given surprise. Because the impact of a shockaffects the economy—and ultimately company performance—over time, theeconomic Wavefront reports the effect of an economic surprise not justas the initial impact on other macro variables but as the total impactover the two years in each variable resulting from the initial surprise.In other words, as FIG. 6 provides an example where if GDP is expectedto be on average 1% higher over the two years 630, then this willnormally be associated with a federal funds rate that is 0.9% higher onaverage over that period 656. In one embodiment, the Wavefront systemaverages over eight quarters because a high percentage of economicshocks appear to dissipate within two years. Another advantage is thatthe economic Wavefront captures the total incremental impact of aparticular surprise across all relevant economic variables across time.This approach reflects both the forward-looking nature of the equitymarket and the inter-relatedness of the overall economy. For the equitymarket, the particular timing of additional GDP over a two-year periodis far less important than the total incremental earnings likely toresult from the revised GDP path. Furthermore, the Wavefront system cancombine Wavefronts to look at complicated series of events. By mixingthe different Wavefronts, the Wavefront system can examine the impact onthe economy if growth expectations were revised up, but oil priceexpectations fell. This allows the investor to examine a wide range ofscenarios, including ones that may not have occurred in the past.Besides providing a very quick summary of the total incremental impactof a particular economic shock, this approach can be used to tailor themodel to particular features of the economic environment. For example,the Wavefront system was used to uncover that the equity market viewsthe consumer as more sensitive to interest rates than usual. Thisinsight makes sense in light of relatively high consumer leverage andthe high cash flows and healthy balance sheets of the corporate sector.To account for this, investors may examine interest rate surprises,currently, and can add in a consumer shock to reflect this feature ofthe current economic environment. This model provides added flexibilityin interpreting the way the market will respond to shifts in economicviews.

Industry Wavefront

Having mapped an economic event 107 into an economic Wavefront 109, theWavefront system may move from the macroeconomic realm into themicroeconomic. To fully move from the macro to the microeconomic, theWavefront system needs to map the developed economic Wavefront 109 intoa series of changes for company performance. Eventually, the maincomponents of return on equity (sales, margin, turnover and leverage),will serve to make that mapping. However, the primary mapping from macroevents to micro performance occurs at the industry level and is based onindustry sales. As such, the Wavefront system will develop an industryWavefront 111 from the economic Wavefront 109. One of the reasons forthis industry mapping is that the relative competitiveness and size ofcompanies may shift fairly radically over time, yet the demand for theoverall industry is far more stable and relates to a fairly limitednumber of key macro variables, which describe the key areas of demand.

FIG. 7 is of an information topology and flow diagram illustratingembodiments of the present invention of an industry Wavefront. FIG. 7basically is a view of the developing Wavefront focusing on elementsthat map 705, 710, 715 the economic Wavefront 109 into the industryWavefront 111. In general the mapping uses sales growth forecasts from anumber of industries 705 by employing an industry classification system710. The sales forecasts are evaluated on their ability to generaterelative industry sales rankings over time for a given point in time715.

In one embodiment, comprehensive modeling of the relationships betweeneconomic events 107 and the economic impacts 109 reduces the need toinclude a large number of economic variables in the industry Wavefrontmodels. For example, a GDP surprise has an effect on industrialproduction as well as on GDP itself. Thus, the Wavefront system is ableto compare the sales response to the GDP shock of two differentindustries even if one is modeled based on GDP and the other based onindustrial production.

In one embodiment, the Wavefront system employs a Modified Standards &Poors (S&P) Global Industry Classification Standard (mGICS), wherein thecodes were modified to provide better economic alignment. In particular,highly idiosyncratic industries such as apparel and footwear retail havebeen combined with other retail industries in a general soft goodscategory. Other industries with highly divergent macro drivers have beensplit into more coherent sub-groups. For example, REITs was split intooffice, residential and industrial groups.

The resulting mGICS information is detailed in Appendix 2. In oneembodiment, the mGICS information is stored in an industries table inthe Wavefront system database. In the embodiment in Appendix 2, thechose industry categories address the following issues:

1. Modeling companies as a group: the industry categories ensure thatcompanies within an industry are economically cohesive enough to bemodeled together.

2. Sufficient number of firms in each group: each industry contains aneconomically meaningful number of firms. A typical classification systemoften contains small groups with less than a handful of firms. Appendix2 recombines these small groups to form an industry of greater economicsignificance.

3. Appendix 2 shows the industry classifications for the currentconstituents of the S&P 500.

In one embodiment, the industry sales are forecast on a smoothed basis(e.g., a 4 quarter percent change) using as compact a macrospecification as possible in order to avoid over-fitting and to providethe optimal estimate of the average smoothed response rather the optimalpoint in time estimate. This forecast basis is supported by the earlierobservation that the equity market prices the incremental shifts inaverage expectations over time rather than fundamentals at some specificpoint in time. The idea in such an embodiment is to generate the bestestimate of the incremental impact of a macro shift on fundamentals ofan industry over a given time period (e.g., the next 2-3 years).However, in an alternative embodiment, the modeling goal of generatingthe best forecast for next quarter also may be achieved by reducing thegiven time period (e.g., the next quarter).

FIG. 8 is of a logic-flow diagram illustrating embodiments of thepresent invention of industry Wavefront development. As alreadymentioned above, the Wavefront system mapped an economic event 107 intoan economic Wavefront 109. The economic Wavefront will now provide itsimpacted economic variables 255 of FIG. 7 as inputs for establishing anindustry Wavefront 111, 805, 810, 815, 850. In furtherance of industryWavefront development 111, the Wavefront system forecasts industry salesgrowth (i.e., for the mGICS industries) as a function of a range ofeconomic drivers. Economic drivers are key economic variables that driveand/or otherwise highly correlate with sales performance for anindustry. Such correlations may be determined for various industry byperforming statistical analysis (e.g., regression/correlation analysis)for the multitude of economic variables and sales performance perindustry 805. Examples of economic drivers include: GDP growth,industrial production growth, consumer spending, interest rates, thedollar, oil prices, etc. As such, for each industry, the Wavefrontsystem selects economic drivers both for their economic plausibility andfor their ability to explain sales performance 810. Appendix 3 detailssales and valuation impacts on the basis of GDP and PCE economicdrivers.

Thus, the industry mapping takes place by feeding the shifts in economicvariables 815 provided/identified by the economic Wavefront 109 asinputs to the selected economic drivers 810 and, thus, create anindustry Wavefront 111. As such, the industry Wavefront describes therelative responses of industry sales to that shock 850. By delineatingthe specific channels through which these industries are affected, withcareful attention to the relative sizes of those effects, the Wavefrontsystem is able to construct trades that emphasize exposures where theportfolio manager has the strongest views and control exposures wheretheir views are not as strong.

In another embodiment, by modeling a relatively wide range of economicvariables, the Wavefront system is able to deal with more complexeconomic shifts. For example, FIG. 9 is of a chart diagram illustratingembodiments of the present invention where industry Wavefronts differfor similar economic events. In the example, the Wavefront systemseparately identifies sensitivity to consumer 905 and investmentspending 910 impacting various industries 915. The figure illustrates,models that estimate each industry's “normal” GDP sensitivity could behighly misleading in assessing relative industry performance on thebasis of diverging sensitivities.

Fundamental Wavefront

FIG. 10 is of an information topology and flow diagram illustratingembodiments of the present invention of a fundamental Wavefront. FIG. 10basically is a view of the developing Wavefront focusing on elementsthat map 1005, 1010, 1015, 1020 the industry Wavefront 111 into thefundamental Wavefront 113. In general the mapping uses industry salesand economic forecasts as inputs to company predictions 1010, companylevel prediction of the DuPont Return on Equity (ROE) 1005, differenteconomic drivers for a single industry (e.g., different drivers forsales, margin, turnover, leverage, etc.) 1015, and different driversacross industries 1020.

The effect of the changes in industry sales 111 are then modeled at thecompany level to assess the likely impact on microeconomic variables113. Microeconomic variables include: company sales, forecasts (i.e.,for any of the microeconomic variables), margins, turnover, andleverage. This produces a set of (company) fundamental Wavefronts asshown in FIG. 10. Again, the focus is on estimating the shift in companyfundamentals due to macroeconomics rather than getting the best estimatefor each company.

There are several added advantages and capabilities to modeling at thecompany level:

1. The results of the fundamental Wavefront may be re-aggregated to anydesired Wavefront level and can be used to construct custom baskets witha large variety of firm-specific or macro criteria (e.g., liquidity,dividends, etc.); and

2. The fundamental Wavefront models automatically adjust for the impactof company-specific structural changes that arise frommergers-and-acquisition activity and other balance sheet actions thatcan dramatically impact equity performance but do not necessarily changethe economic sensitivity of the industry as a whole.

In this way, a resulting fundamental Wavefront describes likely shiftsin microeconomic variables which ultimately stem from an economicsurprise. The industry Wavefront results in industry sales forecasts111. To determine impacts on the fundamental company level 113, theWavefront system may use the industry sales forecasts 111 in conjunctionwith microeconomic variables that are characteristic of driving companysales (e.g., corporate sales, turnover, leverage, etc.).

In choosing which microeconomic variables and which industries providethe best forecasts, the Wavefront system may use statistical analysistools to determine correlations as between actual and predicted industrysales growth along with a collection of microeconomic variables 1105. Inone embodiment, during each quarter, the Wavefront system computes thecorrelation of the actual and predicted sales growth across the 59industries listed in Appendix 3A based on a Pearson and Spearman (i.e.,rank order) basis 1110. The latter measure is less affected by a singleobservation. This provides an estimate of how well the models willcorrectly assess the relative macro-related performance of theseindustries. As such, the Wavefront system can use the best industrysales forecast and company specific microeconomic variables to determinecompany margins 1115. As such, the Wavefront system finishes determiningthe (company) fundamental Wavefront 113, 1150.

FIG. 12 provides a summary of the process outlined above in FIG. 11, 113with the auto industry used as an example. In the first stage 1205,consumer spending, exchange rates, and interest rates determine autoindustry sales 1210. In the second stage 1115, the impact on anindividual auto company's sales 1215 is derived from the industry salesforecast 1220 along with other microeconomic variable values (e.g.,previous year's sale for the company, relative size of the company ascompared to the industry, previous year's margins, asset growth, etc.).Finally, firm sales forecast and relative debt are combined withinformation on capacity utilization to forecast margins 1230.

It should be noted that in comparing different specifications,industry-by-industry goodness-of-fit tests took a secondary role tomeasures that emphasized the ability of the full system of models topredict relative industry movements. This reflects the idea that forgenerating relative value trades, models that produce the betterrelative forecasts are preferable to those that produce the bestforecast of each company's fundamentals in isolation. This approachtends to reward consistency of approach and punish over-fitting.

The results of this effort can be seen in FIG. 13 where the Wavefrontsystem examines the quality of the cross sectional forecasts of theindustry sales model given realized macro variables. As has already beennoted, in one embodiment Pearson and Spearman correlations are run everyquarter to provide rankings of sales forecast bases 1110. The FIG. 13numbers are quite high by general econometric standards for these typesof models, in the neighborhood of 60% in most quarters, and arecertainly high enough to show that the models capture a very highpercentage of the relative macro information that is available toconstruct profitable trades.

Valuation Wavefront

FIG. 14 is of an information topology and flow diagram illustratingembodiments of the present invention of a valuation Wavefront. FIG. 14basically is a view of the developing Wavefront focusing on elementsthat map 1405, 1410 the fundamental Wavefront 113 into the valuationWavefront 115, thereby completing the Wavefront model. In general themapping uses: estimates of the value of an additional unit of margin,turnover, leverage and sales growth (i.e., microeconomic variables)1405; and incorporates market factors such as price, momentum andconsensus earnings estimates 1410. Here, the Wavefront system assesseshow the ripple effects of economic surprises on company performance willaffect the market's perception of the relative value of different stocksand sectors in constructing the valuation Wavefront.

In one embodiment, the Wavefront system uses Market-Based Valuation(MBV) 1540. One aspect of Market-Based Valuation is that valuation isdriven by the relationship between observable company performance andmarket prices. Forecasts are included in MBV, but only in terms ofobservable consensus estimates. The point is that value placed onincremental sales or incremental margins is the average incrementalvalue seen in the market for those changes over the horizon over whichthe incremental value occurs.

The focus is on what the market actually pays for changes in companyfundamentals. FIG. 15 is the valuation model excerpt of FIG. 4, 415 andillustrates that the Wavefront system operates at the company levelvaluing all the observed characteristics of a company, but is basedprimarily on what the market pays for the Return on Equity (ROE). Afeature of this approach is that the Wavefront system breaks down thecomponents of ROE (i.e., sales 1505, margin 1510, turnover 1515,leverage 1520, etc.) and value them separately. This is important bothbecause economic events affect companies' top and bottom linesdifferently and because the market does not reward shifts in eachcomponent equally. As part of the valuation model, the Wavefront systemincludes other factors including: observable forward estimates (adjustedconsensus earnings estimates 1525); risk factors (premiums for companysize and the market as a whole 1535); and price momentum 1530. Thisestablishes a consistent valuation framework across industries andcompanies, whose use extends well beyond the Wavefront applicationpresented here.

Just as the Wavefront system sales models are structured to focus onidentifying relative sales performance, the valuation models arestructured to identify relative value across companies. The estimatesare based on regressions where long-term differences in companies arestatistically neutralized and coefficients are set to optimize thecross-sectional accuracy (i.e., relative valuation). This approach iswell suited to estimating the value of incremental shifts infundamentals even across very different types of firms at differentpoints in time.

In one embodiment, the specific MBV regression techniques are based onpanel regression where long-term differences in companies arestatistically neutralized and coefficients are set to optimize thecross-sectional accuracy (i.e. relative valuation). This type ofregression is well suited to estimating the value of incremental shiftsin fundamentals even across very different types of firms at differentpoints in time.

For ease of cognition, the pricing of observable firm characteristics inMBV may be analogized to quant-based models that predict returns givenfirm characteristics, but the differences are important. A purequant-based approach directly models the timing of equity returns.Because the market anticipates economic developments in ways that varyacross episodes, the timing of when the market realizes the value thatresults from macro events is highly random. This means that models thatfocus on the timing of that recognition process may severelyunderestimate the total value of the macro event, because they estimateonly the part of the value that typically occurs with a specifictemporal relationship to the macro event. Actual traders or portfoliomanagers are likely to have fairly strong views on the timing ofrecognition (e.g., where some given theme/view X is their major focus,or the market has not recognized some theme Y and they believe the valuewill come later this year). As a result, for real-world usage anaccurate assessment of the total value of the trade, although a timingbased valuation model may be used by the Wavefront system in analternative embodiment, the MBV approach, in many cases, is more useful.

Another modeling approach involves feeding shifts in fundamentalsthrough a Discounted Cash Flow (DCF)-type filter to produce estimates ofunderlying value. Here again there are several important differencesbetween the MBV approach and the way in which DCF models are generallyapplied. First, DCF approaches are too sensitive to long run growthestimates and not nearly sensitive enough to short-term deviations. Theyfully value even the most uncertain long-run shifts and fail to fullyvalue the market's typical inference from short-term performance. Intheory, the forecast uncertainty can be addressed with an appropriatediscount rate, but in practice is a difficult problem in itself toconjure a predictive discount rate. Second, the DCF approach does notrecognize that the market actually values different sources of income orcash flow and incremental cash flows in the various industriesdifferently. Also, DCF models cannot account for observed tradingpatterns, such as the very real value of price momentum. So although DCFmodels may be used by the Wavefront system in an alternative embodiment,MBV is more flexible in dealing with such distinctions as its frameworkvalues the components of ROE separately.

Valuation Evaluation

To provide some quantitative feel for the coefficients of thesevaluation models, FIG. 16 shows a breakdown of how the market valuescompany fundamentals in two sectors of the market. The coefficients canbe interpreted as the incremental value in percentage terms of a 1%increase in the observed fundamental. In other words, a 1% change inmargins (or by implication a 1% increase in earnings), holding all elseconstant, would imply a roughly 0.2% increase in the value of a company1605 in the industrials sector but has negligible impact on the value ofa utility company. One interpretation of these differences is that themarket will pay more for shifts that are more likely to provepersistent. FIG. 17, which shows the value of incremental changes inmargins for different sectors at different horizons, supports thisinterpretation. The market tends to pay more for increased margins inmore stable industries than highly variable ones. The differences becomeless pronounced as the horizons are extended. Industries with highlyvariable short-term margin such as utilities (where weather plays amajor short-term role) see a greater increase in the market'swillingness to pay for improved margins as the horizon is extended, thanthose where there are fewer short-term disturbances, presumably becausethe information from improving margins increases.

The Wavefront system's valuation models show good performance underseveral different measures. The R-squared measures shown in FIG. 18 arequite high, indicating that these models capture most of the sources ofdifferences in the relative valuations of companies over the sample. Anadditional way to demonstrate the model's performance is its ability topick stocks. FIG. 19 shows the results of monthly rebalancing a 100stock long-short portfolio based on the Wavefront system's valuationsignal for a sample of the largest companies (e.g., with a marketcapitalization above $250 million). The first row of the tradingback-test shows that the ROE 1905, consensus earnings, and pricemomentum contain quite high value—in the sense of reasonable excessreturns and Sharpe ratios—even if all of the information is historical(i.e. even without a view on the economic or industry outlook beyondconsensus earnings). The historical annualized returns are 12.5% in thefull sample of firms with a market cap above $250 million and 6.8% inthe sample of S&P 500 firms. The difference is due partially to thedifference between companies in the sample, but mostly due to selecting40% of the names in the S&P 500, 100 long and 100 short, while selectingfewer than 15% of the larger samples. Like most quantitative models, MBVgenerates higher returns when picking more extreme stocks. The MBVapproach focuses on detecting relative value in a way different fromother approaches and this may be something we explore further. Row 21910 shows the back-test results for a sample of firms that are stillactively traded a year after they are selected; the effect of thesurvivorship bias is relatively small. The final row of the table usesthe surviving sample to examine the value of one-year forwardinformation within the construct of the MBV model. The results show thataccurate knowledge of forward fundamentals substantially enhancesperformance even in a model that was not optimized for forwardinformation. Thus, forming effective views on the economic outlook andhow such views translate into fundamentals has the power to raisereturns significantly.

Linking the Wavefront all Together

The Wavefront system market-based valuation models provide the last linkin the chain between economic shocks 207 and market returns 240 of FIG.2. They allow investors to value the marginal impact of the variousshifts in fundamentals (from fundamental Wavefronts 113) generated froma given economic event (from economic Wavefronts 109). The result is thevaluation Wavefront. As an example in FIG. 20, a pattern of expectedreturns by company or industry associated with a particular economicsurprise is shown. The Wavefronts capture the difference 2005 both inwhat the market pays sector by sector 2010 and the composition of theincome stream thus providing substantial benefits over DCF approaches.Although the MBV models do not explicitly model the timing of returns,they do incorporate the likely impact of price momentum by including itas a regressor.

One of the advantages of these models is that define a well describedrelationship between economic events and market impact. The models canbe used to improve portfolio managers' ability to implement views andtheir assessment both of what the market has priced and what it islikely to price going forward.

With the Wavefront tools in hand, a discussion of several exampleapplications as implemented by the Wavefront system is discussed. Assuch, the disclosure details how the Wavefront framework is used toconstruct and risk manage trades, and how it may be used to evaluate theeconomic themes being traded in the market.

Building a Trade

FIG. 21 is of a logic-flow diagram illustrating embodiments of thepresent invention to build a trade. To illustrate how the Wavefronttools fit together, what follows is an example of constructing a tradeand tracing the tools' impact through the different stages of the model.Building a trade 120 comprises: forming a macro view 2105, translatingthe view into a valuation Wavefront 2110, and using the valuationWavefront to construct the alternative trades with different riskprofiles and analyze their exposures 2115.

Forming a View

A view is where one thinks the economic environment is going to turn outdifferently from what is currently reflected in the market. In otherwords, one forms a view regarding an economic event that is differentthan what is expected by the market. For example, suppose an investorbelieves that consumer spending growth is likely to slow more sharplythan expected, but that other areas of spending (investment, say) woulddo relatively better offsetting the impact on growth. This wouldeffectively translate into a view where although headline growthexpectations might be roughly correct, the composition of growth couldbe different from what the market is currently expecting. This examplewill be used to run through the tools and illustrate how one may use theWavefront system to come to a set of trades.

In order to run the scenario through the Wavefront system's models, aninvestor would express the core macro view as a series of surprisesrelative to the market's current expectations. This economic scenariorepresents a slowdown in consumer spending that leaves overall growthrelatively unaffected. One starting point is to deliver a negative shockto consumer spending. FIG. 22 illustrates an economic model that showswhat a typical response of the main economic variables is to a negativeconsumer spending surprise. Consumer spending falls, GDP growth isdragged down as a result and this in turn leads to a lower interestrates and a weaker currency than otherwise. The scenario one might wantto describe, however, is one where overall growth does not slow. Tocapture the idea of slower consumer spending without a slower growthoutlook, one needs to neutralize the growth impact of a consumer shock.To do that the negative consumer “surprise” is offset with a positive“surprise” to growth, adding a mix of the two Economic Wavefrontstogether so that the net impact on GDP growth is zero.

FIG. 23 shows the combined set of shifts in the economic outlook thatone would expect to be associated with that combination, using theformat that would normally be shown in a Trade Sheet describing trades.The first column shows the combination of “surprises” that we have fedinto the system to generate our scenario (−2.8 units of consumerspending, +2.0 units of GDP growth). The second column shows what thenet result is for the major economic variables. That combination givesus a situation in which consumer spending growth disappoints (by alittle over 1%), investment spending surprises (by a little over 2%) butoverall views of growth remain relatively unchanged.

Translating the View

To translate the view, one may use our economic scenario to generate avaluation Wavefront 115 of expected returns for the differentindustries, which we use as the basis for our trades. The Wavefrontshows the pattern of returns across industries if views about theeconomic outlook shift in an anticipated way. The shifts implied by theeconomic Wavefront 109 flow into industry sales 111, then intopredictions of company fundamentals 113, and finally into predictedvaluation impacts 115 for each company. Each link in the underlyingchain of models can be examined to ensure confidence in the results.Further the Wavefront can answer questions like: Is the sales effectlarge? Do changes in margin offset the sales impact? What is the effecton net income?

The end result is the valuation Wavefront showing the industriesexpected to do best and those expected to do worst. In one embodiment,the valuation Wavefronts are, relative to the S&P 500, to provide asimple measure of over-/under-performance and to emphasize that theresults are optimized for predicting relative performance rather thanfor predicting absolute returns.

Constructing a Trade

The valuation Wavefront 115 serves as the baseline for constructing thetrade; the long positions will typically come from one extreme and theshort positions from the other. Specific industries are selected basedon secondary risk characteristics. In selecting industries, there isgenerally a trade-off between the expected returns from an industry andthe exposures and risks it adds. One way to exploit our developingexample scenario is to take the industries from the extremes of theWavefront, i.e., picking the longs from the far left hand side (whichhave the highest expected return) and the shorts from the right handside (which have the lowest expected return). This kind of trade is themost leveraged to the macro view and yields high expected returns ifspending surprises favor investment over consumer spending.

FIG. 24 shows some key details of this example trade. The trade isconstructed with seven industries on the long side of the portfolio 2405and six on the short side 2410. The trade has an expected return of 20%2415 based on the view.

One advantage of the Wavefront system tools is that they enable one tolook not just at the likely returns if such a scenario occurs, but alsoallows one to see exposures to macro risks. What happens to thisposition if GDP growth disappoints, or oil prices spike? Answering suchquestions allows investors to tailor the trade in a way that minimizesexposure to risks that worry them, while maximizing exposure to risksthat they think are likely to occur.

FIG. 25A shows the macro exposures to a one-standard-deviation shock tothe main economic variables. Although the expected returns from thistrade are high, our analysis of the macro exposures shows that thiscomes with a price. The trade has major risk exposure to other macrosurprises. At its simplest, the trade has a significant cyclical bias.If growth turned out to be 1% lower than expectations, this could beexpected to shave around 13% off the trade. The trade would also besignificantly exposed to oil prices and interest rates (unexpected risesin rates or oil prices would hurt the trade).

For investors who think that there is upside risk to market views ofoverall growth, the macro exposure is likely to be an added attraction.But for those with a more cautious view, managing that exposure may beimportant.

Manage Risk

One theme of the Wavefront system tools is that there are multiple waysof forming a trade to express any given view. The general point is thateach trade produces a different set of secondary exposures and whatfollows are examples presenting a number of alternatives for a giventrading theme with different risk characteristics that may suitdifferent kinds of investors. As such, a consumer example will serve toillustrate the construction of three trades.

Managing Growth Exposure

One can use the Wavefront techniques to design a trade to manage thegrowth exposure discovered in an initial trade implementation. One wayto do this is to select a combination of long and short positions fromthe Wavefront, which delivers positive returns, but has less exposure tochanging views on GDP growth (either by lowering the exposure in thelong or raising it in the short positions). The Wavefront system toolsenable one to construct such an alternative trade.

FIG. 25B presents the revised industries in the long and short portfolioalong with the risk assessment. The expected return and macro exposuresnow look quite different. The expected return of the trade issignificantly lower than in the previous version, but the macroexposures to the trade have been substantially reduced. By matching thegrowth-sensitivity of the longs and shorts, the exposure of the trade toa slowdown in growth has been reduced.

Both of the previous scenarios assume that the central scenario was onein which overall growth stayed healthy despite the slowing in consumerspending—effectively, the idea that consumers would disappoint, but thatthis would be balanced by stronger-than-expected capex spending. In thescenario where there is no offset (and all other things remain equal) aconsumer disappointment would then also result in slower growth. This isdifferent from the previous trade in which the central economic scenariowas that growth would be unaffected with an aim to minimize risk ifgrowth disappointed. In this case, the central thesis is that afaltering consumer will damage growth and so the underlying economicscenario is different.

To look at this scenario, a straight negative consumer surprise (nolonger offsetting it with a positive GDP growth surprise) may beconstructed. FIG. 22 already illustrated such a macro scenario andcompared it to the growth-neutral version in FIG. 23. The same hit toconsumer spending growth now pulls GDP growth and investment spendinglower and leads the market to revise down its views of the future pathof interest rates and bond yields. The new scenario changes theunderlying Wavefront used to generate the trade. Using the newWavefront, a different set of long-short portfolios can be selected asillustrated in FIG. 25B.

Reading the Market

As well as constructing trades from economic views, the Wavefront modelshave another useful application. In an alternative embodiment, they canbe reversed to see how the market is trading various macro themes. Thisreversal is reflected by the optional dashed arrows moving upwards inthe development of the Wavefront in FIG. 1. This embodiment provides amore sophisticated view on the market's current focus, and on how it isinterpreting particular economic events.

FIG. 1 described the Wavefront mapping macro scenarios to shifts inrelative equity valuation. By reversing that logic, the Wavefront systemcan run regressions on historic equity returns using the Wavefronts asregressors to identify shifts in macro expectations that would bestexplain the observed returns. This provides a useful cross-check on howmuch shifts of relative equity returns can be explained by macrovariables. Effectively, if the pattern of returns across companies showsa high positive correlation with the Wavefront, then this is consistentwith the idea that the market is revising up its views of consumerspending.

FIG. 25C shows an example of this exercise. It illustrates that over theprior two months, the market has traded in a way consistent with adownward revision to GDP growth expectations (i.e., the correlation withour GDP Wavefront is high and negative), though that process hasweakened recently. It also suggests that expectations of oil prices andthe dollar have been revised up over the period.

The application of this “inverse” technology for understanding how themarket is trading macro themes is more subtle in application than mightbe guessed at first glance. For example, there are situations where themarket needs to embed two assessments at once. First, there is thesource of the surprise. Second, there is the mechanism through which thesurprise passes through the economy and the markets. The two assessmentsare sometimes quite different, as illustrated in FIG. 25D, which showsthe Wavefront correlation results for returns between Jan. 27, 2004 andFeb. 5, 2004. This period is interesting because it coincides with theFederal Reserve statement of January 28, at which point the Fed shiftedits language from maintaining an accommodative policy stance “for aconsiderable period,” to being “patient in removing its policyaccommodation.” This shift was widely interpreted as moving forward thetime when the Federal Reserve would raise interest rates.

If one looks at how the equity market actually traded, our exampleanalysis shows that the primary shift in equity markets looked more likea downward shift in expectations of consumer spending than of aninterest rate shift. The interest rate effect of the Federal Reservestatement becomes apparent only after removing the shift in expectationsabout the consumer. This makes sense because lower interest rates haverecently provided an unusually strong support for continued strength inconsumer expenditures and consumer indebtedness is unusually high. Inother words, the market traded the interest rate surprise quitelogically, but not as a naive model might suggest.

Such analysis would allow one to suspect that with the consumer emergingas the “stress point” for the US recovery, that the market response tonegative shocks may generally have more of a consumer flavor than isusual. This example shows the strength of the Wavefront approach fordealing with changes in structure and current market context.

As such, the Wavefront system provides the investor with a greaterunderstanding of how the market has been trading economic themes, whichcan help investors in a number of ways:

1. It may focus their attention on opportunities where the market istrading in a direction that they believe will not be validated byevents.

2. It may help them to identify points at which market focus is shiftingtowards or away from a theme on which they have strong views and toidentify inflection points.

3. It may allow them to identify sectors that have under- or over-tradeda particular theme that the market has traded.

4 Also, it can give them a sense of whether a particular theme ofinterest to them has already been trading in the market.

Assessing Trades

FIGS. 26-43 are tables and charts illustrating embodiments of thepresent invention summarizing the assessment, design and efficacy ofvarious trades; it should be noted that much of the data in the figuresis based on empirical market research. There are many ways to implementa macro view. The complexity of the equity market means that there areusually many ways of getting exposure to the same investment thesis. Agood trade is one that has high leverage to the desired view for a givenlevel of volatility and minimal exposure to other risks. At the stocklevel this often means searching out stocks with the highest leverage tothe desired theme and hoping that this leverage will overwhelm other(incidental) exposures. “Pairs trades” may be used to further focus therisk. At the macro level, similarly, pairs of exchange-traded funds(ETFs) may be used. Although there are many ways to implement a trade,the difference between a good trade and a bad trade for a given macrotheme is often substantial, as is shown here. Different ETF pairs havevarying degrees of effectiveness, and a set of pre-structured Wavefrontbaskets can often increase macro trade focus even further.

Comparing Trading Implementations

There are at least two ways to compare trading implementations.Investors need tools to be able to distinguish good trades from badones. As such, a comparison of different implementations designed usingthe Wavefront models may be achieved by focusing on two such measures ofthe return per unit of risk (explained in greater detail below):

1) The risk/reward ratio. This measures the expected return of the tradegiven the macro view relative to the volatility of returns, which may belikened to a Sharpe ratio.

2) The trade efficiency index. This measures the expected return of thetrade relative to the trade's “incidental risk,” defined as volatilityunrelated to the core macro view.

The above measures capture the key features that identify the besttrades for a given theme.

FIG. 26 is of a table illustrating embodiments of the present inventionsummarizing analysis of a menu of macro trades. It sets out a menu ofmacro trades focusing on five commonly traded major themes: 1. tradingUS economic growth in the US equity market 2605; 2. trading the consumeroutlook in the US equity market 2620; 3. trading interest rates in theUS equity market 2610; 4. trading oil prices in the US equity market2615; 5. trading non-US growth views in the US equity market 2625. Foreach theme, there is a range of alternative ETF implementationsavailable, along with a set of Wavefront baskets. We report the tworisk/reward measures for each. The key message is that there are bigdifferences between the best and worst implementations.

Better risk/reward and higher trade efficiency from managing risks Therisk/reward 2630 and trade efficiency 2635 of the best implementationsare often many times larger than the worst implementations, and theWavefront baskets generally outperform even the best ETFimplementations. It is also possible to improve ETF implementationssubstantially. Measuring and managing incidental (unwanted) risks allowus to create more favorable risk/reward characteristics. Exposure to thetheme can be increased and unwanted macro risks can be offset throughthe identification and selection of industry groups with the rightcombination of exposures, while stock-specific risk can be minimizedthrough diversification. The ability to dig below the sector level turnsout to be a big advantage in focusing risk and one of the reason why theWavefront baskets are helpful. More details regarding the informationset out in FIG. 26 by explaining in greater detail how and why thevarious implementations.

Comparing Trades: Ingredients to Measure Risk/Reward and TradeEfficiency

In order to compare trades, let us focus on two measures. In oneembodiment, the risk/reward ratio 2630 is the ratio of the expectedreturn of the trade to its total volatility. The trade efficiency index2635 is the ratio of the expected return of the trade to its incidentalrisk. The task of generating these measures is not trivial, however, theWavefront models give us the tools to make this attribution:

1. Expected return relative to the core scenario. In one embodiment,this is the Wavefront estimate of the expected return from the economicscenario behind the view. The economic scenarios are assumed to play outover a month and are benchmarked as 2 standard deviation monthly events.

2. Total volatility. We present annualized volatility of monthly returnssince, e.g., 2002.

3. Incidental risk. A trade's volatility comes partly from exposure tothe view and partly from other unwanted exposures. Investors wantexposure to factors associated with their view. It is any otherrisk—what we call incidental risk—that they want to avoid. Comparingreturns to total volatility (as our risk/reward ratio does) may notdistinguish sufficiently between desired and undesired volatility.

One way to measure incidental risk, is to proceed in three stages.First, one assembles estimates of the equity market's change in macroexpectations from the Wavefront Market Monitor. Appendix 5 providesbackground on the Wavefront Market Monitor. These changes are based onobserved relative industry performance. Second, one may use riskanalysis tool to assess the exposures of each implementation to theunderlying macro view. Third, one may combine the two sets ofinformation to produce an estimate of the performance due to theview—essentially a benchmark tracking basket with returns driven bymoves in the macro view. The tracking error around this macro benchmarkworks as a measure of incidental risk. Because the risk/reward ratio andtrade efficiency index are calculated on the assumption that theexpected return is realized over a month, they may be calculatedrelative to the actual non-annualized monthly volatility as follows:

$\frac{{expected}\mspace{14mu}{return}}{{annualized}\mspace{14mu}{volitility}\mspace{14mu}{of}\mspace{14mu}{monthly}\mspace{14mu}{{returns}/\sqrt{12}}}$

Such risk metric tool output is shown in Appendix 4 and allows for afurther break down of sources of risk. Additional risk measures arelisted below in the Risk Metrics Table below (an expanded set of riskmetrics for the various trade implementations are listed in Appendix 4):

Risk Metrics Details Number of long and short positions Number ofindividual stocks in the long and short sides of the proposed tradeimplementation. Number of GS Wavefront industries in long Number ofindustries represented in the long and short position and short sides ofthe proposed trade implementation. “Effective N” of long/short positions“Effective N” is a measure of portfolio concentration. It is the numberof equal- weighted stocks that have the same risk weighted portfolio hasan “Effective N” that is equal to the actual number of stocks in theportfolio. All other portfolios have an “Effective N” that is lower thanthe number of stocks in the portfolio. “Effective N” is calculated asthe reciprocal of the sum of squared portfolio weights. “Effective N” ofbasket The “Effective N” of the long-short basket is a weighted sum ofthe “Effective N” of the two baskets separately. If the long basket andshort basket have very different “Effective N's” but similar dollarweights in the overall portfolio, the “Effective N” of the resultingbasket can be quite low - reflecting the fact that a small number ofstocks on one side of the basket leaves the total portfolio quiteexposed to stock-specific risks. Share of capital in largest fivepositions The summed portfolio weights of the five largest positionsfrom both the long and short side of the trade. Annualized monthlyvolatility Annualized volatility of monthly returns. Annualized macrovolatility Annualized volatility of the portion of monthly returnsgenerated by the full set of Wavefront-measured macroeconomic factors.Annualized view volatility Annualized volatility of the portion ofmonthly returns generated by the specific macroeconomic factor (orfactors) that the given trade implementation is designed to express.Annualized non-macro volatility Annualized volatility of the portion ofmonthly returns not attributed to the full set of Wavefront-measuredmacroeconomic factors. Annualized incidental volatility Annualizedvolatility of the portion of monthly returns not attributed to thespecific macroeconomic factor (or factors) that the given tradeimplementation is designed to express. Daily volatility to monthlyvolatility The ratio of the annualized volatility of daily returns tothe annualized volatility of monthly returns captures the presence oftrending returns, as indicated by a ratio less than 1.

Measuring and Enhancing Risk/Reward in Macro Trades

An effective macro trade comes from maximizing the leverage to the viewand minimizing incidental (unwanted) risks. Incidental risks to a macrotrade can come from exposures to either non-core macro factors or tonon-macro factors. This means that a good macro trade generally hasthree qualities:

1. A strong leverage to the macro view. A trade designed to bepositively levered to economic growth should go up as growth increases,all other considerations being equal.

2. Limited unwanted macro exposures. An effective implementation mayhave little exposure to macro shifts that are not part of the coretrading view.

3. Low stock-specific and idiosyncratic risk. Any equity trade willcarry idiosyncratic risks related to the performance of the industriesand companies in the trade. Diversification limits these risks.

These factors are usually in tension. Stocks or sectors with strongleverage to an underlying view also have leverage to other (unwanted)macro risks. For instance, the automobile industry is rate-sensitive,but it is also leveraged to growth and consumer spending. Similarly,focusing on a small group of stocks or sectors with very high leverageto a view may increase the expected return of a trade but leavesignificant stock-specific risk. The Wavefront tools allow one toquantify the macro exposures across stocks and industries, giving onethe ability to manage these trade-offs. The basic approach is to assessa trade's return to the underlying theme relative to either overallvolatility (risk/reward ratio 2630) or incidental risk (trade efficiencyindex 2635).

Macro exposures differ significantly across the main ETFs. For macrotrading, the desire to diversify away stock-specific risk makes tradingportfolios of stocks rather than individual securities a good startingpoint for managing these trade-offs. One appealing way of gainingexposure to a macro view while diversifying away some stock-specificrisk involves implementations of macro views in equity markets bytrading ETF pairs. Macro factors are an important driver of relativesector and index returns.

FIG. 27 shows the macro exposures for the major economic drivers ofrelative returns (GDP growth, short-term interest rates, and oilprices), where the exposures are all expressed relative to the S&P 500(SPX). The exposures match much (but not all) of the common wisdom aboutthe major ETFs 2705. A 1% increase in expected GDP growth, for instance,is expected to cause the basic materials sector to outperform by 6.0%but consumer staples to underperform by 3.3%. This spread across ETFs,however, hides considerable heterogeneity within them. The ETFs aregenerally broad aggregations of diverse industries. FIG. 27 shows thatthe maximum and minimum industry exposures 2710 to macro factors withineach ETF vary widely. Although some sectors are relatively homogeneous(like consumer staples) in terms of macro risks, many are composites ofgroups of industries that have very different macro characteristics.FIGS. 28 and 29 are examples of the consumer discretionary ETF (XLY)2715 of FIG. 27. Within the sector, there is a wide spread of expectedreturns in response to macro shifts. The response to a GDP shock rangesfrom +3% to −3% FIG. 28, while the response to a shock to the two-yearyield ranges from +1.5% to −9.5% FIG. 29. The sector aggregatesindustries that are defensive to moves in both GDP growth and interestrates (retail and leisure) with industries that are both cyclical and(negatively) responsive to interest rates (durables, homebuilding, andautos). Similar variety can be seen in several other sectors. Althoughmacro trading through ETFs is convenient, the fact that many ETFs areaggregates of industries with quite different macro characteristics maylimit their flexibility. Because they combine industries with a range ofdifferent macro exposures, owning an ETF can dilute the leverage tomacroeconomic views and make it harder to manage macro risks. By“slicing and dicing” at least to the industry level, it is oftenpossible to increase the focus and the efficiency of macro trades.

FIG. 30 shows one example of a Wavefront Growth Basket. Wavefrontbaskets exploit the benefits of “slicing and dicing.” The Wavefrontbaskets, which in one sense are alternative ways to trade macro themes,are designed to exploit the heterogeneity across industries and areusually designed as collections of industries selected for their macroexposure. This long-short 3005, 3010 basket has been selected at theindustry (rather than sector) level to gain leverage to expectations ofrising US GDP growth. FIG. 30 shows the industries in the WavefrontGrowth Basket and their exposures to a scenario in which GDP growthrises. These industries are drawn from six different sectors 3015 withindustries from two sectors (consumer discretionary and technology)actually appearing on both the long and short sides of the trade.Precisely because industries within a sector may have very differentexposures, often risk/reward and trade efficiency can be improved bypicking out the most appropriate industries without regard to sectorboundaries, as will be shown.

Designing Efficient Trades Around Five Macro Themes

One way to illustrate the potential to increase trade efficiency is toconsider alternative implementations across a menu of macro themes. Ineach case, the disclosure sets out the economic scenario that underpinsthe trading theme and compares the different implementations. Infocusing on our two metrics (the risk/reward ratio and the tradeefficiency index), developing five example macro themes will help toillustrate the designing of efficient trades. It should be noted thatthe Risks Metrics Table, above, provides a range of other statistics andsome background on how to generate numerous other economic scenarios.For each of the five macro themes, the Wavefront System sets out ascenario that is expected to play out over a month and use the Wavefrontmodels to generate the expected return of the trade over that period. Asa result, the expected return of the trade that is reported is a returncontingent on the particular scenario that is set out. As such, thereturn may depend on the intensity of the scenario described. A smallershift in macro expectations could leave the ranking of risk/rewardacross different implementations of the theme intact but could reducethe expected return (and also the actual risk/reward ratio and tradeefficiency index) proportionally across all implementations. Thescenarios are scaled so that they represent a 2 standard deviationmonthly event; in other words, the growth scenario is scaled to show amonthly shift in the market's growth expectations that is likely tooccur around 2% of the time. In that sense, each of the scenarios forthe five themes is roughly comparable to each other in terms of howlikely they are to occur.

Theme 1: Trading Growth—Getting “Bang for the Buck”

Investors may wish to seek trades that express views about US economicgrowth prospects. FIG. 31 shows one version of this economic scenario,in which GDP growth rises 0.5% but interest rates remain stable (ourMarket Monitor tells us that a 50-bp shift over a month in the equitymarket's growth expectations is a 2 standard deviation occurrence).

FIG. 32 revisits the menu of alternative trade implementations designedto exploit this kind of growth view. For the ETF trades 3205, thesegenerally involve being long a more “growth-sensitive” ETF against theoverall market or a more “defensive” ETF. The Wavefront Growth Basket3210, described above, and the Wavefront Turbo Growth Basket (which is ahigher leverage version of the same trade) 3215 represent alternativeimplementations. As such, FIG. 32 shows the wide differences in therisk/reward ratio and trade efficiency index and their sources. Thereare at least four differences, which include:

1) There are differences between the various ETF implementations, withthe best ETF implementation (long materials, short consumer staples)3220 three to four times more efficient than the worst.

2) The leverage of the major ETFs relative to the SPX is surprisinglylow, particularly for the tech-oriented trades (QQQQ 3225 and XLK 3230),reflecting the fact that these implementations involve significantexposure to other risks and little leverage to the view.

3) The sector-versus-sector ETF implementations (XLB-XLP 3220 andXLI-XLP 3235) increase this leverage. Although this comes at the expenseof significantly higher volatility and less diversification, they aremore efficient than the ETF trades against the index. By exploitingleverage to the view on the short as well as the long side of the trade(being short a defensive sector rather than the SPX 3240), investors canadd more return than they increase risk.

4) On both measures, the Wavefront baskets are significantly moreefficient than the other baskets. Their expected return to increasingGDP growth is the highest of the implementations 3245. This additionalreturn does not come purely from increased risk, so their leverage tothe view is more focused. Relative to the most efficient ETFimplementations, the Wavefront Growth Basket is also better diversified,which helps to bring the incidental risks down.

Theme 2: Trading the Consumer—Avoiding the Wrong Exposures

Investors may want to leverage to views on the consumer outlook.Equities are arguably the only major asset class where consumer viewscan be effectively expressed, unlike growth and rate views. The menu(see FIG. 26 and FIG. 36) lists a number of different ways of gettingexposure to this theme. One series of ETF implementations involve beinglong a consumer-related sector against another area of the market. Inpractice, there are (at least) two different ways to think about ascenario involving stronger consumer spending. In the pure consumercase, set out in the left-hand panel of FIG. 33, a stronger consumerspending outlook takes place in an environment where growth is unchanged3305 (Scenario 1). In effect, stronger consumer spending comes at theexpense of other areas of spending (in the left-hand panel, principallyinvestment 3305) and overall GDP growth is not altered. There is analternative consumer spending scenario (shown in the right-hand panel)that is arguably more typical 3310 (Scenario 2). In this case, strongerconsumer spending leads to stronger GDP growth (as shown in theright-hand panel). Ideally, a trade to exploit an improving consumeroutlook would perform in both cases.

FIG. 34 highlights the risk/reward metrics relative to the pure consumercase (Scenario 1). The Wavefront Consumer 3410 and Housing Baskets 3415have the highest risk/reward and trade efficiency scores. Those gainscome partly from relatively high expected returns, but at least as muchbecause these baskets have among the lowest incidental risk of theimplementations. The higher incidental risk in the ETF implementations3405 is partly a function of the fact that they have significantexposure to other macro risks, particularly GDP growth. The Wavefrontbaskets, by contrast, have been explicitly designed to have low exposureto both interest rate and GDP growth risk and thus are more purelyleveraged to the consumer theme (see FIG. 35 to see this consumer themeas it responds to growth shocks). In this scenario, the Wavefront basketdesign comprises a set of consumer variables as shown in Appendix 6A and6B. Several of the ETF implementations also perform well, although onceagain the results are generally better for sector-versus-sectorimplementations than implementations that trade one sector against theindex. Some of the classic implementations (long XLY 3420 or RTH 3425,short SPX 3430, for instance) are surprisingly weak, again largelybecause of low expected returns to the underlying view. FIG. 35 showsthe major ETF pairs have sizable negative exposure to GDP growth (theyare generally “defensive”). In fact, the ETF implementations pay off inthe “pure consumer” case (our Scenario 1), not so much because they havesubstantial leverage to the consumer but because they have substantial(negative) leverage to investment and to GDP growth (and in thisscenario, because the consumer is growing and growth is unchanged,investment is falling). In the situation where higher consumer spendingpushes GDP growth up (Scenario 2 above), the macro risks in the ETFimplementations present even more serious problems.

FIG. 36 shows the expected returns in Scenario 2 3310 of FIG. 33. Inthis case, only the Wavefront baskets 3610, 1615 deliver significantpositive returns and many of the alternatives actually lose money. Withgrowth moving up, the cyclical shorts in the ETF implementationsoutperform, which hurts the trade performance. This illustrates theimportance of analyzing and managing secondary macro exposures (which wepresent for the listed trades in the aforementioned Risk Metrics Table).The ETF-based consumer pairs hinge more on the overall growthenvironment than the consumer component. This is not true for theWavefront Consumer Basket 3610 and the Wavefront Housing Basket 3615,which are leveraged primarily to the consumer. This highlights theimportance of having an explicit view. The expected return, risk/rewardratio, and trade efficiency index are all much lower in FIG. 36 than inthe other themes, even for the two Wavefront baskets. These consumertrades are generally not a very effective way of implementing the viewimplicit in Scenario 2.

Theme 3: Trading Oil—where Implementation Matters Less

For equity trades designed to exploit shifts in oil prices, thedifference across alternative implementations is smaller. FIG. 37 setsout a macro scenario in which oil prices rise 3705, driving growth lower3710. FIG. 38 shows three ways to trade the macro scenario in FIG. 37:two different Wavefront baskets 3810 and a standard ETF implementation(long XLE, short SPX) 3815.

FIG. 39 looks across the risk/reward metrics. The differences acrossbaskets are less stark than in earlier cases. The Wavefront Oil Basket3910 with GDP Risk 3915 has the highest expected return 3920 in thisscenario and the highest risk/reward ratio 3925. This basket has beendesigned to be long oil producers and short a group of industries thatare generally hurt by rising oil prices, so both the longs and theshorts are sensitive to rising oil prices. Also, the Wavefront OilBasket 3910 with GDP Risk 3915 scores much lower on the trade efficiencyindex 3930, however, because its incidental risk 3935 is more thandouble that of the alternatives. The problem is that the basket carriessizable cyclical risk because the oil consumers in the short side(airlines, for instance) are generally highly exposed to growth. Asignificant part of the trade's return and volatility comes from growthexposure, which may not be part of the core view. The Wavefront OilBasket 3910 and the XLE-SPX 3940 implementation both avoid this problembecause they are broadly neutral to other macro risks. While they havelower leverage to the scenario, their incidental risk is lower, so moreof their overall volatility is coming from oil prices themselves. Thetwo trades are similar in terms of their risk/reward ratio and tradeefficiency index. It is still possible to “slice and dice” the sector,picking out industries to increase risk/reward. The Wavefront Oil Basketdoes this by focusing on the two energy industries that have the highestleverage to oil prices (E&P and integrateds). This is only a modestchange relative to the XLE-SPX trade, so the increase in risk/reward andtrade efficiency is small. It also comes at the partial cost of reducingthe diversification of the basket, as the Wavefront Oil Basket onlyincludes two of the energy industries (E&P and integrateds).

Theme 4: Trading Rates—Preventing Dilution of the View

Another theme is to trade the impact of shifts in the interest rateenvironment in the equity market. To the extent that higher interestrates hurt growth, it makes more sense to trade this through a growthtrade. FIG. 40, in contrast, shows a scenario that represents a “pure”rates scenario, in which short- and long-term rates move higher butgrowth is unchanged (think of this as the Fed raising rates to capgrowth). Leaving aside the “bond-like” equity trades (trading utilitiesand REITs, for instance) which we regard as close to trading bondsthemselves, the most common sector trades to exploit higher interestrates generally involve going short the financial sector (XLF), or partsof it, against the index.

FIG. 41 compares the various implementations. There is significantvariation across the different implementations, with the Wavefront RatesBasket 4110 delivering the best risk/reward and (particularly) tradeefficiency scores, this time by a substantial margin. The Wavefrontbasket's higher scores come from both higher expected return to ratesand lower incidental risk. Of the two ETF implementations, trading thewhole financial sector is also clearly superior in terms of both returnand incidental risk relative to trading just the regional banks againstthe index. The main problem with the ETF trades is that rates exposurewithin the financial sector varies significantly and parts of othersectors (building products from the industrial sector or parts ofdurable consumer spending) that are highly rate-sensitive are excluded.As a result, the leverage to the rates theme is lower than it could beby being more selective. The Wavefront Rates Basket allows us to carveout the most rate-sensitive areas of different sectors. By addingnon-financial, rate-sensitive industries such as homebuilding andbuilding products and excluding some of the less rate-sensitive parts ofthe financial sector, significant improvements are possible.

Theme 5: Trading Non-US Growth—Getting Exposure to Unique Themes

It can be difficult to get the appropriate level of focus for somecommon themes. In some cases, the problem can be so acute that it ishard to design any kind of sensible sector-based trading implementationfor a particular view. This is likely to be true if the theme cutsacross different parts of the sector and ETF space. One broad categoryof macro themes where those problems appear particularly acute is intrading views on foreign growth in the US equity market. With thegrowing internationalization of corporate performance and increasedsensitivity of US companies to overseas developments, investors arelooking to trade views on non-US growth in the US equity market. Withoutcertain ETF implementations, custom baskets or portfolios may appear tobe the default option for implementing these views.

FIG. 42 shows a Wavefront Foreign Growth Basket designed to allowinvestors to exploit the economic scenario portrayed therein. The basketis designed to be leveraged to shifts in non-US GDP growth, broadlydefined. The industries in the long side of the trade were selected bythe Wavefront models and cross-checked with data on foreign salesexposure. The short side of the basket was used to balance othermacroeconomic risks. The industries cut across a range of sectors, whichis why ETF alternatives are hard to find. Appendix 7 shows theconstruction of this type of basket. An example of such a constructionincludes a Wavefront China Growth Basket, which may be used to gainleverage to Chinese growth, an increasingly important market theme. Thatbasket again cuts across sectors and thus is hard to replicate throughstandard ETFs. FIG. 43 compares foreign growth using trade performancemetrics.

Separating Good Trades from Bad

FIG. 44 shows a mechanism to identify superior trades. The efficiency oftrading implementations matters enormously and varies greatly. OurWavefront baskets offer one way of increasing focus, increasingefficiency, and managing macro-related risks in equity markets. However,even across heavily used ETF strategies, there are significant payoffsto analyzing macro exposures in more detail. This suggests thatinvestors who are committed to ETF implementations can still improvetrade efficiency. Strategies that combine ETFs in more sophisticatedways are also likely to be able to improve efficiency beyond the pairtrades we have considered here, and are considered to be a part of thepresent invention. To that end, the presented themes show instances ofusing Wavefronts and the process of separating a good trade from a badone.

Focus on Risk/Reward and Trade Efficiency

The strategy of analyzing trades in terms of return per unit ofincidental risk is an important insight. Volatility matters, but not allvolatility is created equal. Some risks investors seek out, others theyseek to avoid. A comparison of trades using our risk/reward ratio andtrade efficiency index can help to focus on the strengths and weaknessesof different strategies 4405.

Be Precise About the Theme

It is important to be able to isolate core views from secondary ones. Isa view on the consumer really a view on the consumer or is it a view onGDP growth or interest rates? Expressing views in the most direct waypossible and expressing separate views separately can enhance efficiencyand create greater flexibility to manage positions 4410.

Look for Leverage in Longs as Well as Shorts

In most cases, trade efficiency appears to be improved by looking forleverage to the view in both sides of a long-short trade, rather thansimply trading one side against the index 4415. This is true for many ofthe ETF implementations and generally underpins the Wavefront baskets.

Beware of Other Macro Risks

Primary exposures can be derailed if other macro risks are notrecognized (recall our consumer example). Identifying and managing macrorisks that are not central to the view is one way to improve tradeefficiency 4420. For trading non-macro themes, where any macro risk isprobably unwanted, it may be even more important.

Watch for Stock-Specific Exposure

When expressing a macro view, there is little to be gained from havingsignificant idiosyncratic risk. An important warning sign is determiningif there is a high concentration in a few names 4425. If there is are noidiosyncratic risks, e.g., if there is not a high concentration of riskin a few stocks, then that is indicative of a better trade 4430.Conversely, if there is idiosyncratic risk 4425, then moreequal-weighted portfolios can serve to reduce these problems, i.e.,significant concentration in a few stocks can be a problem with some ETFimplementations.

Macroeconomic Equity Investment Design and Trade System Controller

FIG. 45 is of a block diagram illustrating embodiments of amacroeconomic equity investment design and trade system (the Wavefrontsystem) controller 4501. In this embodiment, the Wavefront systemcontroller 4501 may serve to process, store, search, serve, identify,instruct, generate, match, and/or update recordings, expirations, and/orother related data.

Typically, users, which may be people and/or other systems, engageinformation technology systems (e.g., commonly computers) to facilitateinformation processing. In turn, computers employ processors to processinformation; such processors are often referred to as central processingunits (CPU). A common form of processor is referred to as amicroprocessor. A computer operating system, which, typically, issoftware executed by CPU on a computer, enables and facilitates users toaccess and operate computer information technology and resources. Commonresources employed in information technology systems include: input andoutput mechanisms through which data may pass into and out of acomputer; memory storage into which data may be saved; and processors bywhich information may be processed. Often information technology systemsare used to collect data for later retrieval, analysis, andmanipulation, commonly, which is facilitated through database software.Information technology systems provide interfaces that allow users toaccess and operate various system components.

In one embodiment, the Wavefront system controller 4501 may be connectedto and/or communicate with entities such as, but not limited to: one ormore users from user input devices 4511; peripheral devices 4512; acryptographic processor-device 4528; and/or a communications network4513.

Networks are commonly thought to comprise the interconnection andinteroperation of clients, servers, and intermediary nodes in a graphtopology. It should be noted that the term “server” as used throughoutthis disclosure refers generally to a computer, other device, software,or combination thereof that processes and responds to the requests ofremote users across a communications network. Servers serve theirinformation to requesting “clients.” The term “client” as used hereinrefers generally to a computer, other device, software, or combinationthereof that is capable of processing and making requests and obtainingand processing any responses from servers across a communicationsnetwork. A computer, other device, software, or combination thereof thatfacilitates, processes information and requests, and/or furthers thepassage of information from a source user to a destination user iscommonly referred to as a “node.” Networks are generally thought tofacilitate the transfer of information from source points todestinations. A node specifically tasked with furthering the passage ofinformation from a source to a destination is commonly called a“router.” There are many forms of networks such as Local Area Networks(LANs), Pico networks, Wide Area Networks (WANs), Wireless Networks(WLANs), etc. For example, the Internet is generally accepted as beingan interconnection of a multitude of networks whereby remote clients andservers may access and interoperate with one another.

A the Wavefront system controller 4501 may be based on common computersystems that may comprise, but are not limited to, components such as: acomputer systemization 4502 connected to memory 4529.

Computer Systemization

A computer systemization 4502 may comprise a clock 4530, centralprocessing unit (CPU) 4503, a read only memory (ROM) 4506, a randomaccess memory (RAM) 4505, and/or an interface bus 4507, and mostfrequently, although not necessarily, are all interconnected and/orcommunicating through a system bus 4504. Optionally, the computersystemization may be connected to an internal power source 4586.Optionally, a cryptographic processor 4526 may be connected to thesystem bus. The system clock typically has a crystal oscillator andprovides a base signal. The clock is typically coupled to the system busand various clock multipliers that will increase or decrease the baseoperating frequency for other components interconnected in the computersystemization. The clock and various components in a computersystemization drive signals embodying information throughout the system.Such transmission and reception of signals embodying informationthroughout a computer systemization may be commonly referred to ascommunications. These communicative signals may further be transmitted,received, and the cause of return and/or reply signal communicationsbeyond the instant computer systemization to: communications networks,input devices, other computer systemizations, peripheral devices, and/orthe like. Of course, any of the above components may be connecteddirectly to one another, connected to the CPU, and/or organized innumerous variations employed as exemplified by various computer systems.

The CPU comprises at least one high-speed data processor adequate toexecute program modules for executing user and/or system-generatedrequests. The CPU may be a microprocessor such as AMD's Athlon, Duronand/or Opteron; IBM and/or Motorola's PowerPC; Intel's Celeron, Itanium,Pentium, Xeon, and/or XScale; and/or the like processor(s). The CPUinteracts with memory through signal passing through conductive conduitsto execute stored program code according to conventional data processingtechniques. Such signal passing facilitates communication within theWavefront system controller and beyond through various interfaces.Should processing requirements dictate a greater amount speed, parallel,mainframe and/or super-computer architectures may similarly be employed.Alternatively, should deployment requirements dictate greaterportability, smaller Personal Digital Assistants (PDAs) may be employed.

Power Source

The power source 4586 may be of any standard form for powering smallelectronic circuit board devices such as the following power cells:alkaline, lithium hydride, lithium ion, nickel cadmium, solar cells,and/or the like. Other types of AC or DC power sources may be used aswell. In the case of solar cells, in one embodiment, the case providesan aperture through which the solar cell may capture photonic energy.The power cell 4586 is connected to at least one of the interconnectedsubsequent components of the Wavefront system thereby providing anelectric current to all subsequent components. In one example, the powersource 4586 is connected to the system bus component 4504. In analternative embodiment, an outside power source 4586 is provided througha connection across the I/O 4508 interface. For example, a USB and/orIEEE 1394 connection carries both data and power across the connectionand is therefore a suitable source of power.

Interface Adapters

Interface bus(ses) 4507 may accept, connect, and/or communicate to anumber of interface adapters, conventionally although not necessarily inthe form of adapter cards, such as but not limited to: input outputinterfaces (I/O) 4508, storage interfaces 4509, network interfaces 4510,and/or the like. Optionally, cryptographic processor interfaces 4527similarly may be connected to the interface bus. The interface busprovides for the communications of interface adapters with one anotheras well as with other components of the computer systemization.Interface adapters are adapted for a compatible interface bus. Interfaceadapters conventionally connect to the interface bus via a slotarchitecture. Conventional slot architectures may be employed, such as,but not limited to: Accelerated Graphics Port (AGP), Card Bus,(Extended) Industry Standard Architecture ((E)ISA), Micro ChannelArchitecture (MCA), NuBus, Peripheral Component Interconnect (Extended)(PCI(X)), PCI Express, Personal Computer Memory Card InternationalAssociation (PCMCIA), and/or the like.

Storage interfaces 4509 may accept, communicate, and/or connect to anumber of storage devices such as, but not limited to: storage devices4514, removable disc devices, and/or the like. Storage interfaces mayemploy connection protocols such as, but not limited to: (Ultra)(Serial) Advanced Technology Attachment (Packet Interface) ((Ultra)(Serial) ATA(PI)), (Enhanced) Integrated Drive Electronics ((E)IDE),Institute of Electrical and Electronics Engineers (IEEE) 1394, fiberchannel, Small Computer Systems Interface (SCSI), Universal Serial Bus(USB), and/or the like.

Network interfaces 4510 may accept, communicate, and/or connect to acommunications network 4513. Through a communications network 113, theWavefront system controller is accessible through remote clients 4533 b(e.g., computers with web browsers) by users 4533 a. Network interfacesmay employ connection protocols such as, but not limited to: directconnect, Ethernet (thick, thin, twisted pair 10/100/1000 Base T, and/orthe like), Token Ring, wireless connection such as IEEE 802.11a-x,and/or the like. A communications network may be any one and/or thecombination of the following: a direct interconnection; the Internet; aLocal Area Network (LAN); a Metropolitan Area Network (MAN); anOperating Missions as Nodes on the Internet (OMNI); a secured customconnection; a Wide Area Network (WAN); a wireless network (e.g.,employing protocols such as, but not limited to a Wireless ApplicationProtocol (WAP), I-mode, and/or the like); and/or the like. A networkinterface may be regarded as a specialized form of an input outputinterface. Further, multiple network interfaces 4510 may be used toengage with various communications network types 4513. For example,multiple network interfaces may be employed to allow for thecommunication over broadcast, multicast, and/or unicast networks.

Input Output interfaces (I/O) 4508 may accept, communicate, and/orconnect to user input devices 4511, peripheral devices 4512,cryptographic processor devices 4528, and/or the like. I/O may employconnection protocols such as, but not limited to: Apple Desktop Bus(ADB); Apple Desktop Connector (ADC); audio: analog, digital, monaural,RCA, stereo, and/or the like; IEEE 1394a-b; infrared; joystick;keyboard; midi; optical; PC AT; PS/2; parallel; radio; serial; USB;video interface: BNC, coaxial, composite, digital, Digital VisualInterface (DVI), RCA, RF antennae, S-Video, VGA, and/or the like;wireless; and/or the like. A common output device is a television set145, which accepts signals from a video interface. Also, a videodisplay, which typically comprises a Cathode Ray Tube (CRT) or LiquidCrystal Display (LCD) based monitor with an interface (e.g., DVIcircuitry and cable) that accepts signals from a video interface, may beused. The video interface composites information generated by a computersystemization and generates video signals based on the compositedinformation in a video memory frame. Typically, the video interfaceprovides the composited video information through a video connectioninterface that accepts a video display interface (e.g., an RCA compositevideo connector accepting an RCA composite video cable; a DVI connectoraccepting a DVI display cable, etc.).

User input devices 4511 may be card readers, dongles, finger printreaders, gloves, graphics tablets, joysticks, keyboards, mouse (mice),remote controls, retina readers, trackballs, trackpads, and/or the like.

Peripheral devices 4512 may be connected and/or communicate to I/Oand/or other facilities of the like such as network interfaces, storageinterfaces, and/or the like. Peripheral devices may be audio devices,cameras, dongles (e.g., for copy protection, ensuring securetransactions with a digital signature, and/or the like), externalprocessors (for added functionality), goggles, microphones, monitors,network interfaces, printers, scanners, storage devices, video devices,video sources, visors, and/or the like.

It should be noted that although user input devices and peripheraldevices may be employed, the Wavefront system controller may be embodiedas an embedded, dedicated, and/or monitor-less (i.e., headless) device,wherein access would be provided over a network interface connection.

Cryptographic units such as, but not limited to, microcontrollers,processors 4526, interfaces 4527, and/or devices 4528 may be attached,and/or communicate with the Wavefront system controller. A MC68HC16microcontroller, commonly manufactured by Motorola Inc., may be used forand/or within cryptographic units. Equivalent microcontrollers and/orprocessors may also be used. The MC68HC16 microcontroller utilizes a16-bit multiply-and-accumulate instruction in the 16 MHz configurationand requires less than one second to perform a 512-bit RSA private keyoperation. Cryptographic units support the authentication ofcommunications from interacting agents, as well as allowing foranonymous transactions. Cryptographic units may also be configured aspart of CPU. Other commercially available specialized cryptographicprocessors include VLSI Technology's 33 MHz 6868 or SemaphoreCommunications' 40 MHz Roadrunner 184.

Memory

Generally, any mechanization and/or embodiment allowing a processor toaffect the storage and/or retrieval of information is regarded as memory4529. However, memory is a fungible technology and resource, thus, anynumber of memory embodiments may be employed in lieu of or in concertwith one another. It is to be understood that a the Wavefront systemcontroller and/or a computer systemization may employ various forms ofmemory 4529. For example, a computer systemization may be configuredwherein the functionality of on-chip CPU memory (e.g., registers), RAM,ROM, and any other storage devices are provided by a paper punch tape orpaper punch card mechanism; of course such an embodiment would result inan extremely slow rate of operation. In a typical configuration, memory4529 will include ROM 4506, RAM 4505, and a storage device 4514. Astorage device 4514 may be any conventional computer system storage.Storage devices may include a drum; a (fixed and/or removable) magneticdisk drive; a magneto-optical drive; an optical drive (i.e., CDROM/RAM/Recordable (R), ReWritable (RW), DVD R/RW, etc.); and/or otherdevices of the like. Thus, a computer systemization generally requiresand makes use of memory.

Module Collection

The memory 4529 may contain a collection of program and/or databasemodules and/or data such as, but not limited to: operating systemmodule(s) 4515 (operating system); information server module(s) 4516(information server); user interface module(s) 4517 (user interface);Web browser module(s) 4518 (Web browser); database(s) 4519;cryptographic server module(s) 4520 (cryptographic server); theWavefront system module(s) 4535; and/or the like (i.e., collectively amodule collection). These modules may be stored and accessed from thestorage devices and/or from storage devices accessible through aninterface bus. Although non-conventional software modules such as thosein the module collection, typically, are stored in a local storagedevice 4514, they may also be loaded and/or stored in memory such as:peripheral devices, RAM, remote storage facilities through acommunications network, ROM, various forms of memory, and/or the like.

Operating System

The operating system module 4515 is executable program code facilitatingthe operation of a the Wavefront system controller. Typically, theoperating system facilitates access of I/O, network interfaces,peripheral devices, storage devices, and/or the like. The operatingsystem may be a highly fault tolerant, scalable, and secure system suchas Apple Macintosh OS X (Server), AT&T Plan 9, Be OS, Linux, Unix,and/or the like operating systems. However, more limited and/or lesssecure operating systems also may be employed such as Apple MacintoshOS, Microsoft DOS, Palm OS, Windows2000/2003/3.1/95/98/CE/Millenium/NT/XP (Server), and/or the like. Anoperating system may communicate to and/or with other modules in amodule collection, including itself, and/or the like. Most frequently,the operating system communicates with other program modules, userinterfaces, and/or the like. For example, the operating system maycontain, communicate, generate, obtain, and/or provide program module,system, user, and/or data communications, requests, and/or responses.The operating system, once executed by the CPU, may enable theinteraction with communications networks, data, I/O, peripheral devices,program modules, memory, user input devices, and/or the like. Theoperating system may provide communications protocols that allow theWavefront system controller to communicate with other entities through acommunications network 4513. Various communication protocols may be usedby the Wavefront system controller as a subcarrier transport mechanismfor interaction, such as, but not limited to: multicast, TCP/IP, UDP,unicast, and/or the like.

Information Server

An information server module 4516 is stored program code that isexecuted by the CPU. The information server may be a conventionalInternet information server such as, but not limited to Apache SoftwareFoundation's Apache, Microsoft's Internet Information Server, and/orthe. The information server may allow for the execution of programmodules through facilities such as Active Server Page (ASP), ActiveX,(ANSI) (Objective−) C (++), C#, Common Gateway Interface (CGI) scripts,Java, JavaScript, Practical Extraction Report Language (PERL), Python,WebObjects, and/or the like. The information server may support securecommunications protocols such as, but not limited to, File TransferProtocol (FTP); HyperText Transfer Protocol (HTTP); Secure HypertextTransfer Protocol (HTTPS), Secure Socket Layer (SSL), and/or the like.The information server provides results in the form of Web pages to Webbrowsers, and allows for the manipulated generation of the Web pagesthrough interaction with other program modules. After a Domain NameSystem (DNS) resolution portion of an HTTP request is resolved to aparticular information server, the information server resolves requestsfor information at specified locations on a the Wavefront systemcontroller based on the remainder of the HTTP request. For example, arequest such as http://123.124.125.126/myInformation.html might have theIP portion of the request “123.124.125.126” resolved by a DNS server toan information server at that IP address; that information server mightin turn further parse the http request for the “/myInformation.html”portion of the request and resolve it to a location in memory containingthe information “myInformation.html.” Additionally, other informationserving protocols may be employed across various ports, e.g., FTPcommunications across port 21, and/or the like. An information servermay communicate to and/or with other modules in a module collection,including itself, and/or facilities of the like. Most frequently, theinformation server communicates with the Wavefront system database 4519,operating systems, other program modules, user interfaces, Web browsers,and/or the like.

Access to the Wavefront system database may be achieved through a numberof database bridge mechanisms such as through scripting languages asenumerated below (e.g., CGI) and through inter-application communicationchannels as enumerated below (e.g., CORBA, WebObjects, etc.). Any datarequests through a Web browser are parsed through the bridge mechanisminto appropriate grammars as required by the Wavefront system. In oneembodiment, the information server would provide a Web form accessibleby a Web browser. Entries made into supplied fields in the Web form aretagged as having been entered into the particular fields, and parsed assuch. The entered terms are then passed along with the field tags, whichact to instruct the parser to generate queries directed to appropriatetables and/or fields. In one embodiment, the parser may generate queriesin standard SQL by instantiating a search string with the properjoin/select commands based on the tagged text entries, wherein theresulting command is provided over the bridge mechanism to the Wavefrontsystem as a query. Upon generating query results from the query, theresults are passed over the bridge mechanism, and may be parsed forformatting and generation of a new results Web page by the bridgemechanism. Such a new results Web page is then provided to theinformation server, which may supply it to the requesting Web browser.

Also, an information server may contain, communicate, generate, obtain,and/or provide program module, system, user, and/or data communications,requests, and/or responses.

User Interface

The function of computer interfaces in some respects is similar toautomobile operation interfaces. Automobile operation interface elementssuch as steering wheels, gearshifts, and speedometers facilitate theaccess, operation, and display of automobile resources, functionality,and status. Computer interaction interface elements such as check boxes,cursors, menus, scrollers, and windows (collectively and commonlyreferred to as widgets) similarly facilitate the access, operation, anddisplay of data and computer hardware and operating system resources,functionality, and status. Operation interfaces are commonly called userinterfaces. Graphical user interfaces (GUIs) such as the Apple MacintoshOperating System's Aqua, Microsoft's Windows XP, or Unix's X-Windowsprovide a baseline and means of accessing and displaying informationgraphically to users.

A user interface module 4517 is stored program code that is executed bythe CPU. The user interface may be a conventional graphic user interfaceas provided by, with, and/or atop operating systems and/or operatingenvironments such as Apple Macintosh OS, e.g., Aqua, Microsoft Windows(NT/XP), Unix X Windows (KDE, Gnome, and/or the like), mythTV, and/orthe like. The user interface may allow for the display, execution,interaction, manipulation, and/or operation of program modules and/orsystem facilities through textual and/or graphical facilities. The userinterface provides a facility through which users may affect, interact,and/or operate a computer system. A user interface may communicate toand/or with other modules in a module collection, including itself,and/or facilities of the like. Most frequently, the user interfacecommunicates with operating systems, other program modules, and/or thelike. The user interface may contain, communicate, generate, obtain,and/or provide program module, system, user, and/or data communications,requests, and/or responses.

Web Browser

A Web browser module 4518 is stored program code that is executed by theCPU. The Web browser may be a conventional hypertext viewing applicationsuch as Microsoft Internet Explorer or Netscape Navigator. Secure Webbrowsing may be supplied with 128 bit (or greater) encryption by way ofHTTPS, SSL, and/or the like. Some Web browsers allow for the executionof program modules through facilities such as Java, JavaScript, ActiveX,and/or the like. Web browsers and like information access tools may beintegrated into PDAs, cellular telephones, and/or other mobile devices.A Web browser may communicate to and/or with other modules in a modulecollection, including itself, and/or facilities of the like. Mostfrequently, the Web browser communicates with information servers,operating systems, integrated program modules (e.g., plug-ins), and/orthe like; e.g., it may contain, communicate, generate, obtain, and/orprovide program module, system, user, and/or data communications,requests, and/or responses. Of course, in place of a Web browser andinformation server, a combined application may be developed to performsimilar functions of both. The combined application would similarlyaffect the obtaining and the provision of information to users, useragents, and/or the like from the Wavefront system enabled nodes. Thecombined application may be nugatory on systems employing standard Webbrowsers.

Cryptographic Server

A cryptographic server module 4520 is stored program code that isexecuted by the CPU 4503, cryptographic processor 4526, cryptographicprocessor interface 4527, cryptographic processor device 4528, and/orthe like. Cryptographic processor interfaces will allow for expeditionof encryption and/or decryption requests by the cryptographic module;however, the cryptographic module, alternatively, may run on aconventional CPU. The cryptographic module allows for the encryptionand/or decryption of provided data. The cryptographic module allows forboth symmetric and asymmetric (e.g., Pretty Good Protection (PGP))encryption and/or decryption. The cryptographic module may employcryptographic techniques such as, but not limited to: digitalcertificates (e.g., X.509 authentication framework), digital signatures,dual signatures, enveloping, password access protection, public keymanagement, and/or the like. The cryptographic module will facilitatenumerous (encryption and/or decryption) security protocols such as, butnot limited to: checksum, Data Encryption Standard (DES), EllipticalCurve Encryption (ECC), International Data Encryption Algorithm (IDEA),Message Digest 5 (MD5, which is a one way hash function), passwords,Rivest Cipher (RC5), Rijndael, RSA (which is an Internet encryption andauthentication system that uses an algorithm developed in 1977 by RonRivest, Adi Shamir, and Leonard Adleman), Secure Hash Algorithm (SHA),Secure Socket Layer (SSL), Secure Hypertext Transfer Protocol (HTTPS),and/or the like. Employing such encryption security protocols, theWavefront system may encrypt all incoming and/or outgoing communicationsand may serve as node within a virtual private network (VPN) with awider communications network. The cryptographic module facilitates theprocess of “security authorization” whereby access to a resource isinhibited by a security protocol wherein the cryptographic moduleeffects authorized access to the secured resource. In addition, thecryptographic module may provide unique identifiers of content, e.g.,employing and MD5 hash to obtain a unique signature for an digital audiofile. A cryptographic module may communicate to and/or with othermodules in a module collection, including itself, and/or facilities ofthe like. The cryptographic module supports encryption schemes allowingfor the secure transmission of information across a communicationsnetwork to enable a the Wavefront system module to engage in securetransactions if so desired. The cryptographic module facilitates thesecure accessing of resources on the Wavefront system and facilitatesthe access of secured resources on remote systems; i.e., it may act as aclient and/or server of secured resources. Most frequently, thecryptographic module communicates with information servers, operatingsystems, other program modules, and/or the like. The cryptographicmodule may contain, communicate, generate, obtain, and/or provideprogram module, system, user, and/or data communications, requests,and/or responses.

The Wavefront System Database

A the Wavefront system database module 4519 may be embodied in adatabase and its stored data. The database is stored program code, whichis executed by the CPU; the stored program code portion configuring theCPU to process the stored data. The database may be a conventional,fault tolerant, relational, scalable, secure database such as Oracle orSybase. Relational databases are an extension of a flat file. Relationaldatabases consist of a series of related tables. The tables areinterconnected via a key field. Use of the key field allows thecombination of the tables by indexing against the key field; i.e., thekey fields act as dimensional pivot points for combining informationfrom various tables. Relationships generally identify links maintainedbetween tables by matching primary keys. Primary keys represent fieldsthat uniquely identify the rows of a table in a relational database.More precisely, they uniquely identify rows of a table on the “one” sideof a one-to-many relationship.

Alternatively, the Wavefront system database may be implemented usingvarious standard data-structures, such as an array, hash, (linked) list,struct, structured text file (e.g., XML), table, and/or the like. Suchdata-structures may be stored in memory and/or in (structured) files. Inanother alternative, an object-oriented database may be used, such asFrontier, ObjectStore, Poet, Zope, and/or the like. Object databases caninclude a number of object collections that are grouped and/or linkedtogether by common attributes; they may be related to other objectcollections by some common attributes. Object-oriented databases performsimilarly to relational databases with the exception that objects arenot just pieces of data but may have other types of functionalityencapsulated within a given object. If the Wavefront system database isimplemented as a data-structure, the use of the Wavefront systemdatabase 4519 may be integrated into another module such as theWavefront system module 4535. Also, the database may be implemented as amix of data structures, objects, and relational structures. Databasesmay be consolidated and/or distributed in countless variations throughstandard data processing techniques. Portions of databases, e.g.,tables, may be exported and/or imported and thus decentralized and/orintegrated.

In one embodiment, the database module 4519 includes several tables 4519a-h. A macroeconomic variables table 4519 a includes fields such as, butnot limited to: variable name (which may be a key field—e.g., variablenames may include: GDP, oil prices, etc.), expected value, currentvalue, historic value, associated macroeconomic variables, associatedindustry, time period, time duration, and/or the like. A microeconomicvariables table 4519 b includes fields such as, but not limited to:variable name (which may be a key field—e.g., variable names mayinclude: sales revenue, margin, profit, costs, turnover, leverage, priceto earnings ratio, etc.), expected value, current value, historic value,associated industry, associated company, time period, time duration,and/or the like. A time effects table 4519 c includes fields such as,but not limited to: variable name (which may be a key field), timeperiod, time duration, decay curve, and/or the like. An industries table4519 d includes fields such as, but not limited to: industry code (whichmay be a key field), industry name, associated macroeconomic variables,associated aggregate microeconomic variables, associated companies,and/or the like. A companies table 4519 e includes fields such as, butnot limited to: company code (which may be a key fiend—e.g., a tickersymbol), company name, associated microeconomic variables, and/or thelike. An affected industries patterns table 4519 f includes fields suchas, but not limited to: associated macroeconomic variable (which may bekey), industry code, industry name, and/or the like. An affectedcompanies patterns table 4519 g includes fields such as, but not limitedto: associated industries (which may be key), company code, companyname, and/or the like. An affected macroeconomic variables patternstable 4519 h includes fields such as, but not limited to: variance fromexpectation for event, macroeconomic event variable (the combination ofthe preceding two fields may be key), associated macroeconomic variable,and/or the like.

In one embodiment, the Wavefront system database may interact with otherdatabase systems. For example, employing a distributed database system,queries and data access by the Wavefront system modules may treat thecombination of the Wavefront system database, an integrated datasecurity layer database as a single database entity.

In one embodiment, user programs may contain various user interfaceprimitives, which may serve to update the Wavefront system. Also,various accounts may require custom database tables depending upon theenvironments and the types of clients a the Wavefront system may need toserve. It should be noted that any unique fields may be designated as akey field throughout. In an alternative embodiment, these tables havebeen decentralized into their own databases and their respectivedatabase controllers (i.e., individual database controllers for each ofthe above tables). Employing standard data processing techniques, onemay further distribute the databases over several computersystemizations and/or storage devices. Similarly, configurations of thedecentralized database controllers may be varied by consolidating and/ordistributing the various database modules 4519 a-j. The Wavefront systemmay be configured to keep track of various settings, inputs, andparameters via database controllers.

A the Wavefront system database may communicate to and/or with othermodules in a module collection, including itself, and/or facilities ofthe like. Most frequently, the Wavefront system database communicateswith a the Wavefront system module, other program modules, and/or thelike. The database may contain, retain, and provide informationregarding other nodes and data.

The Wavefront System

A the Wavefront system module 4535 is stored program code that isexecuted by the CPU. The Wavefront system affects accessing, obtainingand the provision of information, services, transactions, and/or thelike across various communications networks.

The Wavefront system enables investors to design trades around macrothemes. Part of the approach is a linked set of models calledWavefronts. Also, the Wavefront system may employ spreadsheets,databases, and/or other computational engines a basis for itscomputations and data interchange. The Wavefront system tracks viewinghabits, enables the purchasing of extended views of programs, removesexpired media programming content, and more. The Wavefront systemcoordinates with the Wavefront system database to identifyinterassociations as between macro and microeconomic variables, and/orany related information, such as industry sectors and specificcompanies.

A the Wavefront system module enabling access of information betweennodes may be developed by employing standard development tools such as,but not limited to: (ANSI) (Objective−) C (++), Apache modules, binaryexecutables, database adapters, Java, JavaScript, mapping tools,procedural and object oriented development tools, PERL, Python, shellscripts, SQL commands, web application server extensions, WebObjects,and/or the like. In one embodiment, the Wavefront system server employsa cryptographic server to encrypt and decrypt communications. A theWavefront system module may communicate to and/or with other modules ina module collection, including itself, and/or facilities of the like.Most frequently, the Wavefront system module communicates with a theWavefront system database, operating systems, other program modules,and/or the like. The Wavefront system may contain, communicate,generate, obtain, and/or provide program module, system, user, and/ordata communications, requests, and/or responses.

Distributed the Wavefront System

The structure and/or operation of any of the Wavefront system nodecontroller components may be combined, consolidated, and/or distributedin any number of ways to facilitate development and/or deployment.Similarly, the module collection may be combined in any number of waysto facilitate deployment and/or development. To accomplish this, one mayintegrate the components into a common code base or in a facility thatcan dynamically load the components on demand in an integrated fashion.

The module collection may be consolidated and/or distributed incountless variations through standard data processing and/or developmenttechniques. Multiple instances of any one of the program modules in theprogram module collection may be instantiated on a single node, and/oracross numerous nodes to improve performance through load-balancingand/or data-processing techniques. Furthermore, single instances mayalso be distributed across multiple controllers and/or storage devices;e.g., databases. All program module instances and controllers working inconcert may do so through standard data processing communicationtechniques.

The configuration of the Wavefront system controller will depend on thecontext of system deployment. Factors such as, but not limited to, thebudget, capacity, location, and/or use of the underlying hardwareresources may affect deployment requirements and configuration.Regardless of if the configuration results in more consolidated and/orintegrated program modules, results in a more distributed series ofprogram modules, and/or results in some combination between aconsolidated and distributed configuration, data may be communicated,obtained, and/or provided. Instances of modules consolidated into acommon code base from the program module collection may communicate,obtain, and/or provide data. This may be accomplished throughintra-application data processing communication techniques such as, butnot limited to: data referencing (e.g., pointers), internal messaging,object instance variable communication, shared memory space, variablepassing, and/or the like.

If module collection components are discrete, separate, and/or externalto one another, then communicating, obtaining, and/or providing datawith and/or to other module components may be accomplished throughinter-application data processing communication techniques such as, butnot limited to: Application Program Interfaces (API) informationpassage; (distributed) Component Object Model ((D)COM), (Distributed)Object Linking and Embedding ((D)OLE), and/or the like), Common ObjectRequest Broker Architecture (CORBA), process pipes, shared files, and/orthe like. Messages sent between discrete module components forinter-application communication or within memory spaces of a singularmodule for intra-application communication may be facilitated throughthe creation and parsing of a grammar. A grammar may be developed byusing standard development tools such as lex, yacc, XML, and/or thelike, which allow for grammar generation and parsing functionality,which in turn may form the basis of communication messages within andbetween modules. Again, the configuration will depend upon the contextof system deployment.

The entirety of this disclosure (including the Cover Page, Title,Headings, Field, Background, Summary, Brief Description of the Drawings,Detailed Description, Claims, Abstract, Figures, and otherwise) shows byway of illustration various embodiments in which the claimed inventionsmay be practiced. The advantages and features of the disclosure are of arepresentative sample of embodiments only, and are not exhaustive and/orexclusive. They are presented only to assist in understanding and teachthe claimed principles. It should be understood that they are notrepresentative of all claimed inventions. As such, certain aspects ofthe disclosure have not been discussed herein. That alternateembodiments may not have been presented for a specific portion of theinvention or that further undescribed alternate embodiments may beavailable for a portion is not to be considered a disclaimer of thosealternate embodiments. It will be appreciated that many of thoseundescribed embodiments incorporate the same principles of the inventionand others are equivalent. Thus, it is to be understood that otherembodiments may be utilized and functional, logical, organizational,structural and/or topological modifications may be made withoutdeparting from the scope and/or spirit of the disclosure. As such, allexamples and/or embodiments are deemed to be non-limiting throughoutthis disclosure. Also, no inference should be drawn regarding thoseembodiments discussed herein relative to those not discussed hereinother than it is as such for purposes of reducing space and repetition.For instance, it is to be understood that the logical and/or topologicalstructure of any combination of any program modules (a modulecollection), other components and/or any present feature sets asdescribed in the figures and/or throughout are not limited to a fixedoperating order and/or arrangement, but rather, any disclosed order isexemplary and all equivalents, regardless of order, are contemplated bythe disclosure. Furthermore, it is to be understood that such featuresare not limited to serial execution, but rather, any number of threads,processes, services, servers, and/or the like that may executeasynchronously, concurrently, in parallel, simultaneously,synchronously, and/or the like are contemplated by the disclosure. Assuch, some of these features may be mutually contradictory, in that theycannot be simultaneously present in a single embodiment. Similarly, somefeatures are applicable to one aspect of the invention, and inapplicableto others. In addition, the disclosure includes other inventions notpresently claimed. Applicant reserves all rights in those presentlyunclaimed inventions including the right to claim such inventions, fileadditional applications, continuations, continuations in part,divisions, and/or the like thereof. As such, it should be understoodthat advantages, embodiments, examples, functional, features, logical,organizational, structural, topological, and/or other aspects of thedisclosure are not to be considered limitations on the disclosure asdefined by the claims or limitations on equivalents to the claims.

1. A computer-implemented method for designing securities transactions,comprising: receiving securities portfolio data on a computer system;receiving macro-economic event data, wherein the macro-economic eventdata includes data associated with a plurality of macro-economicvariables; analyzing the received securities portfolio data and themacro-economic event data to determine a plurality of risks andexposures to economic and market factors using said computer system,wherein said analysis includes: generating by the computer systemeconomic wavefront data based on the received macro-economic event data,wherein the economic wavefront data describes total incremental impactof the macro-economic event on the plurality of macro-economicvariables; generating by the computer system industry wavefront databased on the economic wavefront data, wherein the industry wavefrontdata describes relative response of industry sales to the economicwavefront data; generating by the computer system fundamental wavefrontdata based on the industry wavefront data, wherein the fundamentalwavefront data describes impact of the industry wavefront data atcompany level on microeconomic variables; and generating by the computersystem valuation wavefront data based on the fundamental wavefront data,wherein the valuation wavefront data describes the impact of thefundamental wavefront data on relative value of the securitiesportfolio; reconfiguring by the computer system said securitiesportfolio based on the valuation wavefront data resulting from theanalysis to manage the plurality of risks and exposures to the economicand market factors.
 2. The method of claim 1, further comprisingdetermining the total incremental impact of the macro-economic event onthe plurality of macro-economic variables by: determining a timeinterval that the macro-economic event affects the plurality ofmacro-economic variables; obtaining macro-economic variable values forthe determined time interval; and calculating the total incrementalimpact for the plurality of macro-economic variables for the determinedtime interval based on the obtained macro-economic variable values. 3.The method of claim 1, further comprising determining the relativeresponse of industry sales to the economic wavefront data by correlatingthe plurality of macro-economic variables with the industry salesresponse.
 4. The method of claim 3 further comprising: identifying mostpredictive macro-economic variables from the plurality of macro-economicvariables based on the correlation; deriving company margin from theidentified most predictive macro-economic variables; and determiningvaluation of the company based on the derived company margin.
 5. Themethod of claim 4, wherein the valuation is based on market-basedvaluation.
 6. A computer-implemented method for designing securitiestransactions, comprising: receiving on a computer system securitiesportfolio data; receiving on said computer system economic wavefrontdata, wherein said economic wavefront data includes data associated witha macro-economic event and anticipated impact of the macro-economicevent on a plurality of macro-economic variables; receiving valuationwavefront data on said computer system, wherein said valuation wavefrontdata includes data associated with an anticipated impact of the economicwavefront data on industry performance and company fundamentals; andanalyzing by the computer system the received securities portfolio data,the economic wavefront data and the valuation wavefront data todetermine a plurality of risks and exposures to economic and marketfactors using said computer system; and reconfiguring said securitiesportfolio based on said analysis using said computer system to managethe plurality of risks and exposures to the economic and market factors.7. The method of claim 6 wherein said analysis comprises: developing awavefront model linking the economic and valuation wavefront data usingsaid computer system to determine impact of the macro-economic event onvaluation of the securities portfolio.
 8. The method of claim 6 furthercomprising: aggregating company-level impact results on an industrylevel using said computer system to develop macro-economic positionsthat are diversified away from a specific company risk and focused ondesired macro-economic exposures.
 9. A system for designing securitiestransactions, comprising: a memory; a processor disposed incommunication with said memory, and configured to issue a plurality ofprocessing instructions stored in the memory, wherein the processorissues instructions to: receive securities portfolio data; receivemacro-economic event data, wherein the macro-economic event dataincludes data associated with a plurality of macro-economic variables;analyze the received securities portfolio data and the macro-economicevent data to determine a plurality of risks and exposures to economicand market factors, wherein said analysis includes instructions to:generate economic wavefront data based on the received the receivedmacro-economic event data, wherein the economic wavefront data describestotal incremental impact of the macro-economic event on the plurality ofmacro-economic variables; generate industry wavefront data based on theeconomic wavefront data, wherein the industry wavefront data describesrelative response of industry sales to the economic wavefront data;generate fundamental wavefront data based on the industry wavefrontdata, wherein the fundamental wavefront data describes impact of theindustry wavefront data at company level on microeconomic variables; andgenerate valuation wavefront data based on the fundamental wavefrontdata, wherein the valuation wavefront data describes the impact of thefundamental wavefront data on relative value of the securitiesportfolio; reconfigure said securities portfolio based on the valuationwavefront data resulting from the analysis to manage the plurality ofrisks and exposures to the economic and market factors.
 10. A system fordesigning securities transactions, comprising: a memory; a processordisposed in communication with said memory, and configured to issue aplurality of processing instructions stored in the memory, wherein theprocessor issues instructions to: receive securities portfolio data;receive economic wavefront data, wherein the economic wavefront dataincludes data associated with a macro-economic event and anticipatedimpact of the macro-economic event on a plurality of macro-economicvariables; receive valuation wavefront data, wherein said valuationwavefront data includes data related to anticipated impact of theeconomic wavefront data on industry performance and companyfundamentals; analyze the received securities portfolio data, theeconomic wavefront data and the valuation wavefront data to determine aplurality of risks and exposures to economic and market factors; andreconfigure said securities portfolio based on said analysis to managethe plurality of risks and exposures to the economic and market factors.11. A non-transitory medium readable by a processor to design securitiestransactions, comprising: processor-executable program instructionsresiding thereon, wherein the processor-executable program instructionsare executable by the processor to: receive securities portfolio data;receive macro-economic event data, wherein the macro-economic event dataincludes data associated with a plurality of macro-economic variables;analyze the received securities portfolio data and the macro-economicevent data to determine a plurality of risks and exposures to economicand market factors, wherein said analysis includes instructions to:generate economic wavefront data based on the received the receivedmacro-economic event data, wherein the economic wavefront data describestotal incremental impact of the macro-economic event on the-plurality ofmacro-economic variables; generate industry wavefront data based on theeconomic wavefront data, wherein the industry wavefront data describesrelative response of industry sales to the economic wavefront data;generate fundamental wavefront data based on the industry wavefrontdata, wherein the fundamental wavefront data describes impact of theindustry wavefront data at company level on microeconomic variables; andgenerate valuation wavefront data based on the fundamental wavefrontdata, wherein the valuation wavefront data describes the impact of thefundamental wavefront data on relative value of the securitiesportfolio; reconfigure said securities portfolio based on the valuationwavefront data resulting from the analysis to manage the plurality ofrisks and exposures to the economic and market factors.
 12. Anon-transitory medium readable by a processor to design securitiestransactions, comprising: processor-executable program instructionsresiding thereon, wherein the processor-executable program instructionsare executable by the processor to: receive securities portfolio data;receive economic wavefront data, wherein said economic wavefront dataincludes data associated with a macro-economic event and anticipatedimpact of the macro-economic event on a plurality of macro-economicvariables; receive valuation wavefront data on said computer system,wherein said valuation wavefront data includes data associated with ananticipated impact of the economic wavefront data on industryperformance and company fundamentals; analyze the received securitiesportfolio data, the economic wavefront data and the valuation wavefrontdata to determine a plurality of risks and exposures to economic andmarket factors using said computer system; and reconfigure saidsecurities portfolio based on said analysis using said computer systemto manage the plurality of risks and exposures to the economic andmarket factors.
 13. An apparatus to design securities transactions,comprising: a memory; a processor disposed in communication with saidmemory, and configured to issue a plurality of processing instructionsstored in the memory, wherein the instructions issue signals to: receivesecurities portfolio data; receive macro-economic event data, whereinthe macro-economic event data includes data associated with a pluralityof macro-economic variables; analyze the received securities portfoliodata and the macro-economic event data to determine a plurality of risksand exposures to economic and market factors, wherein said analysisincludes instructions to: generate economic wavefront data based on thereceived the received macro-economic event data, wherein the economicwavefront data describes total incremental impact of the macro-economicevent on the plurality of macro-economic variables; generate industrywavefront data based on the economic wavefront data, wherein theindustry wavefront data describes relative response of industry sales tothe economic wavefront data; generate fundamental wavefront data basedon the industry wavefront data, wherein the fundamental wavefront datadescribes impact of the industry wavefront data at company level onmicroeconomic variables; and generate valuation wavefront data based onthe fundamental wavefront data, wherein the valuation wavefront datadescribes the impact of the fundamental wavefront data on relative valueof the securities portfolio; reconfigure said securities portfolio basedon the valuation wavefront data resulting from the analysis to managethe plurality of risks and exposures to the economic and market factors.14. An apparatus to design securities transactions, comprising: amemory; a processor disposed in communication with said memory, andconfigured to issue a plurality of processing instructions stored in thememory, wherein the instructions issue signals to: receive securitiesportfolio data; receive economic wavefront data, wherein the economicwavefront data includes data associated with a macro-economic event andanticipated impact of the macro-economic event on a plurality ofmacro-economic variables; receive valuation wavefront data, wherein saidvaluation wavefront data includes data related to anticipated impact ofthe economic wavefront data on industry performance and companyfundamentals; analyze the received securities portfolio data, theeconomic wavefront data and the valuation wavefront data to determine aplurality of risks and exposures to economic and market factors; andreconfigure said securities portfolio based on said analysis to managethe plurality of risks and exposures to the economic and market factors.